Rapid urbanization coupled with industrial growth escalated energy demands, leading to large-scale generation of fly ash as a byproduct of coal-fired thermal power plants. The annual production of fly ash has reached around 592 million tonnes in 2022 and is forecasted to reach around 1090 million tonnes by 2032. Approximately 50 % of this generated ash is used in construction, and the rest is stored in the ash disposal facilities. The finer particles in the disposed ash, ranging from 10 μm to 100 μm, are highly susceptible to wind erosion, causing severe dust nuisance in the surrounding environment. Conventional dust suppression techniques, such as water spraying and chemical suppressants, are resource-intensive and, in certain conditions, are found to be ineffective. Enzyme-induced calcite precipitation (EICP) is an emerging biochemical process that has great potential in mitigating dust nuisance and soil erosion. The calcium carbonate crystals precipitated during this biochemical process result in the enhancement of surface strength, thus mitigating wind erosion. Enhancement of the surface strength of fly ash was studied using EICP treatment under varying conditions of dry density, moisture content, and substrate composition. EICP-treated samples were found to exhibit a substantial increase in penetration resistance of about 13.6 times that of pristine samples after 7 days of treatment. The strength enhancement leading to dust suppression was also validated using a wind tunnel experiment and particulate matter sampling. The results highlight the combined effect of moisture availability, density, and substrate composition in optimizing EICP performance. These findings demonstrate the potential of EICP as a sustainable and efficient solution to enhance the surface strength of fly ash deposits, thereby mitigating dust emissions from various industrial disposal facilities.

To support decision-making processes aimed at mitigating flood impacts and groundwater depletion, this study aims to quantify the potential recharge effects of riparian restoration of floodplain functions. We hypothesize that since 1985, groundwater recharge in the Northern Rio Grande Basin has declined due to reductions in flood-irrigated agriculture, and that current recharge suitability in semi-arid basins is significantly associated with soil type/hydraulic conductivity, land use/land cover, precipitation, slope, and hydro-geomorphic opportunity. Using similar basins with known recharge, these parameters will be used to predict recharge potential, and in the Santa Cruz Basin, the top predicted suitability zones will align with geomorphic parameters known to support recharge (e.g., floodplains and irrigated lands), more often than random distribution would predict. Recharge estimation is approached in two stages. First, reductions in recharge since 1985 are estimated from observed declines in flood-irrigated agriculture. To reconstruct historical irrigation patterns, we developed a training dataset through interviews with local farmers, identifying irrigated and non-irrigated parcels across diverse land uses. Annual NDVI values (2005–2024) were extracted from Landsat 5, 7, 8, and 9 imagery using Google Earth Engine (GEE), and sensor-specific NDVI thresholds were derived to improve classification consistency. These thresholds were then applied to classify irrigated land annually from 1985 to 2024. The preliminary results revealed a peak in irrigated area during the 1990s, followed by a decline in the 2000s, and partial recovery in the 2010s. Second, potential recharge from restoration is quantified by applying recharge rates reported in donor basins to Santa Cruz subareas identified through spatial suitability analysis. ArcGIS-based Weighted Overlay Analysis and Spatially Weighted Regression are used to link soil, land cover, precipitation, slope, and hydro-geomorphic opportunity with recharge suitability. Mann–Kendall tests and hydrological datasets are employed to validate temporal trends. This approach yields a spatial framework that (i) quantifies recharge decline linked to reduced irrigation since 1985, and (ii) identifies priority areas where restoration of floodplain connectivity and managed flooding could enhance recharge. The findings provide a transferable method to assess recharge decline and restoration potential in semi-arid basins facing water scarcity and land-use change.

The Mid-Atlantic Bight continental shelf is a key frontier for offshore wind development but is underlain by complex, less-studied subsurface geology. A mosaic of fluvial, coastal, and marine sediments shaped by repeated sea-level fluctuations and river incision has created buried paleochannels with highly variable sediment properties. These features have direct implications for foundation stability and early-stage risk assessment in unleased wind energy areas off Delaware and Maryland.

Relative to the rest of the soil matrix, soil preferential flow (PF) disproportionately influences large-scale processes like groundwater recharge and solute flux via rapid transport of water through soil pore spaces. Despite the ubiquity of PF occurrence across land uses and climates, there is lack of understanding around the feedbacks between PF generation and carbon transport and transformation, both of which result from abiotic (e.g., water content, clay content) and biotic (e.g., root and microbial activity) processes. We can assess the depth distribution of soil carbon parameters in relation to PF occurrence through soil profiles to decipher the effects of PF generation on carbon dynamics. Recent work from our group shows that the probability of PF occurrence increases with rainfall intensity, net primary production, and clay content. In addition, total rainfall amount increases PF propagation to deeper soil layers. We hypothesize that these same factors increase the transport of carbon deeper into the subsoil and influence carbon transformation at depth. Specifically, we hypothesize that high peak rainfall intensity, net primary production, and clay content will be associated with greater availability of extractable organic carbon relative to total soil organic carbon (SOC) at depth due to rapid transport via PF, high root abundance, and high retention of mineral-associated SOC, respectively. To identify the dominant drivers of PF and the resulting dynamic feedbacks with soil carbon, we synthesized metrics of soil PF, carbon, and enzyme activity from observations at eight U.S. sites spanning climatic gradients, land-cover types, and soil orders. We derived PF metrics using a soil water velocity threshold method based on observations of high frequency soil moisture and precipitation data. PF occurred when the observed soil water flux exceeded the saturated hydraulic conductivity of the soil matrix estimated from a pedotransfer function. Preliminary findings indicate that land-cover is a primary control on extractable DOC availability and propagation with soil depth. PF may also be associated with DOC transformations, given the coupling of land cover and net primary production. Understanding which, and to what extent, abiotic and biotic factors promote generation of PF through feedbacks with soil carbon dynamics advances our knowledge of the potential effects of changing climate and land use on water and carbon fluxes through soils.

The assessment of wind energy potential in mountainous regions (particularly over forested areas) remains a challenge due to limited observations, less well understood interactions of wind with varying topography and forests, and limited accuracy of numerical models in complex terrain. To address these limitations, this study focuses on the University of Virginia’s Mountain Lake Biological Station (MLBS) in the Appalachians of southwestern Virginia. Immersed in an 18 m-tall deciduous forest, the MLBS has been collecting wind observations at multiple levels of a 29 m tower (operated by the National Ecological Observatory Network, NEON) since at least 2017. The NEON tower is located on a small plateau surrounded by valleys and ridges. The present study uses the tower wind measurements to investigate the seasonal and the diurnal variability of vertical wind profiles within and above the canopy. Primarily west-northwest wind regimes are observed throughout each year, with generally higher wind speeds during winter within and above the canopy, and during nighttime hours above the canopy. Seasonal differences in wind speeds (exceeding the typical turbine cut-in threshold of approximately 3 m/s) suggest an increased wind energy potential in wintertime.

The Amazon Basin, one of the most biodiverse regions on Earth, is shaped by complex interactions between climate, hydrology, and forest dynamics. It’s strongly influenced by seasonal flooding, soil type, and forest succession. Deforestation has dominated discussions of land use change in recent decades; However, increasing attention is being given to forest recovery and passive restoration processes. Understanding where and how forests recover is critical for guiding conservation policy and monitoring long-term ecosystem resilience, an approach aligned with the Mosaico do Baixo Rio Negro’s regional conservation framework. This study uses Landsat and Planetscope imagery to explore patterns of vegetation and surface moisture change from 1990 to 2024 along the Amazon River near Manaus, Brazil. Spectral indices derived from Landsat imagery were used to assess vegetation conditions and surface moisture across ten representative assessment sites, establishing baseline environmental patterns. These metrics were then applied across four broader restoration mosaics to evaluate landscape-scale vegetation recovery and ecosystem change. Spatial structure, including fragmentation and edge effects, were also analyzed. This data was combined to identify trends in forest succession, detect areas of passive regeneration, and evaluate ecological stability. PlanetScope imagery supported visual interpretation of finer-scale landscape changes. Trends in spectral index values were extracted, mapped, and categorized across all assessment and restoration zones to visualize temporal and spatial variability over the 34-year period. These patterns provide a quantitative foundation for analyzing vegetation structure, surface moisture dynamics, and regeneration processes across tropical forest systems, supporting long-term monitoring and conservation planning.

New and upcoming satellite missions such as NASA’s 2024 Plankton, Aerosol, Cloud Ocean Ecosystem (PACE) and 2028 Surface Biology and Geology (SBG) missions will produce hyperspectral imagery with great potential for monitoring water quality. To better understand how hyperspectral data represent lake biogeochemical processes and states, the LakeView campaign entailed simultaneous airborne hyperspectral imaging and comprehensive in situ sampling of Lake Mendota in Madison, WI. 14 sampling events in March through November, 2024 captured the limnological conditions of Lake Mendota as it passed through seasonal phases that have distinct water optical characteristics. Such phases are driven by ecosystem functions such as food webs, nutrient cycling, and metabolism. To evaluate relationships between water quality and remote sensing reflectance, a canonical correlation analysis was performed on two groups of variables: (1) the optically active water constituents (dissolved organic carbon, total suspended solids, chlorophyll, and phycocyanin) and (2) visible wavelengths. Clusters of hyperspectral data on canonical variate axes align with known phases of Lake Mendota’s annual cycle: non-cyanobacteria phytoplankton in the spring, the clear water phase, and a cyanobacteria-dominated summer into fall. This airborne campaign helps to uncover the spectral shapes and magnitudes correlated with field measurements of coupled biogeochemical variables over time. Such information should drive us closer to monitoring dynamic lake ecosystems with hyperspectral satellites.

As meltwater and precipitation drain through glacier ice into subglacial networks, the water generates seismic energy upon interaction with the bed. These signals, known as glaciohydraulic tremor, are detectable by seismometers on or near the ice. Tremor has been shown to act as a proxy for subglacial water flux, revealing patterns in melt variability, sediment transport, and large-scale glacier dynamics. Despite strong empirical correlations, the quantitative relationship between tremor characteristics and discharge remains poorly constrained. Meanwhile, existing theoretical models lack rigorous validation. This research integrates stream gauge records and passive seismic observations with hydrological modelling to develop an empirical model of the tremor-discharge relationship. Preliminary results reaffirm the correlation between tremor amplitude and discharge, while revealing that existing weather-dependent melt models cannot adequately capture discharge patterns observed in Southeast Alaska. Incorporating an inverse model from the observed discharge using multivariate linear regression reduces uncertainty associated with weather variability and topographic controls. Expanding the analysis across multiple glaciers in Alaska and Central Europe allows for evaluation and correction of local factors, such as glacier size, ice geometry, and receiver-conduit distance, on tremor behavior to better compare results with theoretical models. Ultimately, this work aims to advance the capacity to use seismic methods as a reliable proxy for subglacial discharge, offering valuable insights into large-scale glacier processes and enhancing our ability to predict glacier change.

Lumped basin representations are a cornerstone of hydrologic modeling, supporting applications ranging from regional prediction in ungauged basins (PUBs) to conceptual and semi-distributed frameworks. They also play a critical role in regional parameter calibration and in training “universal” deep-learning models for river-flow forecasting. Traditionally, lumped descriptors summarize the average characteristics of a watershed — such as its shape, mean slope, mean elevation, or the proportion of hydrologically relevant land-cover types. However, these approaches overlook both spatial heterogeneity and the spatial organization of distributed basin characteristics, which can significantly influence hydrologic response. In fact, distinct spatial configurations can yield similar average values yet produce markedly different surface runoff behaviors. We introduce a novel methodological framework for deriving a new class of lumped metrics based on hydrologic connectivity, that are able to capture differences in the spatial arrangement of surface characteristics. These metrics facilitate more meaningful comparisons across basins and offer a hydrologically informed approach to integrating heterogeneous spatial data into complex deep-learning models, enhancing both reliability and interpretability. We generate a CONUS-wide dataset of connectivity-based descriptors and demonstrate its utility by training an ensemble of long-short term memory (LSTM) models for hydrologic forecasting, benchmarking their performance against the same model trained with traditional basin descriptors. We further examine model sensitivity to individual input variables to assess how the LSTM ensemble leverages the information contained in distinct connectivity-based basin metrics.

The existing sewage system in India emits dangerous gases such as carbon dioxide (CO2), hydrogen sulfide (H2S), and methane (CH4). These gases pose health risks, sometimes resulting in fire hazards or, in severe instances, fatalities of manual scavengers. The Government of India introduced The Employment of Manual Scavengers and Construction of Dry Latrines (Prohibition) Act in 1993 in an attempt to ban manual scavenging. However, casualties related to such practice can still be witnessed in different states of India. There have been recent studies related to the usage of machines or robots to address this issue. But what if we could avoid the need for someone or something to enter the manhole by altering the drainage system itself? A new drainage system called the ‘Rotating siever pipe system’ was designed and proposed to prevent manual scavenging. The name ‘Rotating siever pipe system’ is relatively self-explanatory. ‘Siever pipe’ is a new term for the pipe in the sewerage system, with mesh-like structures that sieve particles. This helps liquid and solid waste travel and be collected separately. The term ‘rotating’ represents the pipe system rotates around for self-cleansing and prevents clogging. This system is expected to eliminate the need for manual scavengers, ensuring the safety of both scavengers and the environment.

Gridded meteorological datasets based on in-situ observations are among the most trusted datasets for climate monitoring. Here, we present the initial results in the development of a new 5 km daily gridded temperature and precipitation dataset for the Continental United States (CONUS). The dataset was developed using a state-of-the-art deep learning model architecture called Neural Processes (NP), which predicts both the quantity of interest and the associated prediction uncertainty. In our application, we tasked the NP model to ingest daily weather records from the Global Historical Climatology Network and to fill in the gaps between the stations to achieve a gap-free gridded output for precipitation, maximum, minimum, and average temperature. Evaluation was done for five test years that were excluded from the model training. Respectively, the average of the daily mean absolute error and Pearson correlation across CONUS were 1.21 mm and 0.86 for precipitation, 1.09°C and 0.97 for maximum temperature, 1.43°C and 0.95 for minimum temperature, 0.96°C and 0.97 for average temperature. The prediction uncertainty ranged from 0 to 4.5°C for the temperature variables and showed dependency on the station density. Finally, the reconstructed dataset had very good spatial agreement with nClimGrid, PRISM and Daymet.

Small agricultural reservoirs play a critical role in meeting irrigation and livestock water needs during dry spells. However, systematic mapping of these reservoirs and estimating their storage capacity remain challenging due to their small size and high temporal variability caused by evaporative losses and frequent water withdrawals [1, 2]. This study integrates Sentinel-1 and Sentinel-2 satellite remote sensing data to develop a global detection methodology for agricultural reservoirs smaller than 0.1 km². Water pixels are identified using region-specific Otsu’s dynamic thresholding applied to Normalized Difference Water Index (NDWI) derived from Sentinel-2 and VV-polarized backscatter from Sentinel-1 data. A comprehensive ground-truth database was developed through manual reservoir delineation, enabling machine learning algorithm development and validation across diverse geographic and climatic regions. This research addresses a critical data gap in the extent and storage estimation of small reservoirs, which are often overlooked in existing datasets of surface water bodies. It supports key applications including irrigation management, water budgeting, drought resilience planning, and hydrological and climate modeling, with particular relevance to water-stressed regions where small reservoirs serve as essential agricultural water resources [3].

We present a detailed investigation of Universal Time (UT) variability in the high-latitude topology of the Earth’s magnetosphere, using the Tsyganenko T96 model to trace magnetic field lines and determine the location of the Open/Closed Boundary (OCB). Our analysis spans all 24 UT hours and evaluates the invariant latitude (INVLAT) of the OCB across seasons and geomagnetic conditions, with particular attention to the dawn-dusk asymmetries near the cusp and cleft regions. We identify a persistent MLT sector (~04:00–07:00 MLT) where open/closed classification becomes sensitive to field-line tracing limits (RLIM), leading to spurious INVLAT “spikes” under quiet conditions. Reducing RLIM to 25 RE successfully mitigates this effect and aligns with values supported by prior literature. Our results demonstrate that even under fixed solar wind and geomagnetic inputs, the OCB exhibits systematic UT-dependent variation due to Earth’s rotation and dipole tilt. This variability is most pronounced in cusp-adjacent regions and has implications for space weather modeling, especially in forecasting polar cap access of energetic particles and HF radio absorption. These findings enhance the temporal fidelity of high-latitude boundary models and offer new diagnostic tools for refining SEP cutoff predictions in operational settings.

The Arteni Volcanic Complex lies within the Ararat Plain, within the Aragats Volcanic Province (AVP) of western Armenia, and is noted for its remarkable diversity of obsidian types. K-Ar dates show Early Pleistocene ages of 1.26 to 1.60 Ma, while fission track dating yields slightly younger ages of 1.17 to 1.38 Ma. The Arteni Volcanic Complex was formed through successive eruptions from two adjacent centers: Mets Arteni and Pokr Arteni, both dome-shaped rhyolitic volcanoes. The complex consists of rhyolitic domes, dome-coulees, necks, fissures, extrusions, and dikes—typical of high-viscosity silicic volcanism. The Arteni rhyolitic lava flow extends a remarkable 8 km from the summit area of Mets Arteni. Covering an area of ~15 km², its volume is estimated at 0.6 km³. The unit consists of a basal layer of pyroclastics and crystalline rhyolitic-dacitic rocks, followed by crystal-clear obsidian, and capped by vesicular water-rich perlite. The obsidian typically contains between 0.06 and 0.2 wt% H₂O, while the overlying perlite contains up to 4 wt% H₂O. Although rhyolitic melts are usually highly viscous, the Arteni flow’s excessive length suggests lower viscosity due to temperature, composition and water content during emplacement. Consequently, we modeled viscosity using 850-970 °C obtained from similar rheological calculations for the Dry Fountain obsidian complex located 22 km north of Yerevan, Armenia. We note that the upper perlite layers of the Aragats flow contain 3.5–4 wt% H₂O. If the underlying obsidian formed by rapid degassing under lithostatic pressure applied by the overlying perlite, the original melt likely had a water content of ~3.8 wt%. For this water content and temperature, the viscosity of Arteni rhyolite was 3.86–4.79 log η (Pa·s), much lower than anhydrous obsidian (7.45–9.18 log η Pa·s). We suggest that this low viscosity led to the exceptional length of the rhyolite flow due to depolymerization of melt structure as well as lack of phenocrysts.

Land-atmosphere coupling influences critical climate processes from regional drought to seasonal predictability, yet existing methodologies struggle to capture the full spectrum of teleconnection mechanisms. Traditional approaches—coordinated multi-model experiments, such as GLACE comparing interactive versus fixed land surface variables, forecast initialization studies, and idealized extreme event simulations—each have distinct drawbacks in signal detection capability, often requiring unrealistic perturbations that likely change model climatology. We present an enhanced system identification approach that overcomes such limitations through optimized signal design and improved response analysis. Our methodology injects artificial signals with zero-mean characteristics that preserve climatological states while enabling unprecedented detection of atmospheric responses via sliding regression. Unlike conventional step-response experiments, this approach enables detection of atmospheric responses while minimizing system perturbation through small neutral forcing. Results demonstrate successful capture of dynamic atmospheric responses to surface forcing. Results demonstrate successful capture of dynamic atmospheric responses to surface forcing. The technique traces propagation pathways from localized surface heating to upper-tropospheric circulation patterns. This approach enables climate model evaluation, seasonal prediction enhancement, and mechanistic understanding of climate feedbacks, with future applications extending to assess teleconnection capabilities in AI-based climate models such as NeuralGCM and Ai2 Climate Emulator.

Low-frequency (LF) components are critical for resolving deep structures and mitigating cycle skipping in full-waveform inversion (FWI). However, seismic surveys often lack sufficient LF content below 4 Hz due to source and receiver limitations. We propose and evaluate a data-driven method for LF extrapolation using Bidirectional Long Short-Term Memory (BiLSTM) networks trained exclusively on synthetic data. The model learns to reconstruct missing LF content from conventional airgun data by observing how high-frequency traces relate to their low-frequency-rich counterparts. The training dataset comprises 100,000 synthetic traces generated by convolving randomized reflectivity series with estimated airgun and tuned pulse source (TPS) signatures. Additive noise is introduced before and after convolution to simulate acquisition variability and unmodeled effects. The network is trained to map airgun-band inputs to TPS-band outputs using a hybrid loss function combining time-domain mean squared error with a frequency-domain regularization term, encouraging spectral recovery below 4 Hz. To assess generalization, we apply the trained model to a real field dataset acquired with dual-source acquisition (airgun and TPS). Despite no exposure to real data during training, the BiLSTM predicts low-frequency components that closely match the TPS ground truth. Spectral analysis shows significant enhancement below 4 Hz, and a 3 Hz low-pass filter reveals strong event alignment between the model output and TPS recordings (Figure). These results demonstrate the potential of synthetic training pipelines and recurrent architectures to enrich legacy seismic datasets and support LF-sensitive applications like FWI. The method offers a lightweight, field-deployable tool requiring no changes to acquisition hardware.

Dispersion imaging is a crucial step in multichannel analysis of surface wave surveys for estimating shear-wave velocity profiles. Traditional wavefield transformation methods such as the f-k, τ-p, and phase-shift techniques assume planar wavefronts and often require preprocessing steps to suppress noise and compensate for geometric attenuation. One common step is trace normalization or amplitude balancing, applied in either the time or frequency domain to ensure equal weighting across sensors. In contrast, cylindrical beamforming inherently accounts for geometric spreading by applying square root of distance-based shading weights to the frequency-domain wavefield, making additional normalization unnecessary. Field data comparisons show that using phase-shift or f-k methods with trace normalization versus using cylindrical beamforming without normalization can lead to a significant shift in the relative energy distribution between the fundamental and higher modes. This variation is not intrinsic to the transformation technique itself but results directly from the differing preprocessing strategies applied. Such effects are particularly evident in heterogeneous subsurface conditions where multiple modes propagate with varying amplitudes. These observations underscore the need for careful consideration of preprocessing choices, as they directly influence mode sensitivity and ultimately impact the reliability of surface wave dispersion analysis.

Short-period exoplanets may induce highly beamed electron cyclotron maser radio emission from their host stars via unipolar induction. This mechanism functions in the Jupiter-Io system, which powers some of Jupiter’s auroral activity emitted from UV to radio wavelengths. It provides an indirect means to characterize the magnetism of the orbiting satellite or exoplanet. We present results of a ~10-year campaign to monitor the 4 GHz radio emission from the M9 brown dwarf TVLM 513 465-46 using Arecibo Observatory. This substellar object periodically emits bursty 4-8 GHz radio emission on its P=1.96 hr rotation period, and may host exoplanets that magnetically interact with it. We investigate long-term temporal and luminosity trends in the radio emission, and use them to evaluate hypotheses ranging from inducement via star-planet interactions, to the existence of solar-like magnetic activity cycles.