Decreased water quality in coastal environments due to land alterations by human activities has caused stressed and degraded coral reefs worldwide. The consequences of reduced water quality are not limited to coral reefs but also affect the quality of people’s lives by increasing the incidence of diseases, so areas highly impacted have been prioritized for management. The Guánica Bay Watershed Management Plan was developed to reduce the non-point sources of pollution that arrive at the bay and to protect adjacent coral reefs, however, 15 years have passed since its creation, and management actions have not been evaluated. This study aimed to assess the effectiveness of the management actions implemented in the Guánica Bay watershed. We described temporal trends (2002–2008 and 2016–2022) of remotely sensed diffuse attenuation coefficient at 490 nm (Kd490), a water clarity indicator, in one managed (Guánica Bay) and three non-managed (Guayanilla Bay, Descalabrado River, and Guanajibo River) estuaries in south and southwest Puerto Rico. This was achieved by integrating ocean-color satellite imagery from MERIS-Envisat and OLCI-Sentinel-3 sensors sampled using a beyond-Before-After-Control-Impact (beyond-BACI) approach. The analysis for the beyond-BACI found significant differences between periods (before and after) but the changes were unique to each location within the estuaries. The southern estuaries showed similar temporal trends, all having a peak in 2018 related to the passage of Hurricanes Irma and Maria and a trough in 2020 related to the Covid-19 pandemic. Kd490 did not decrease in Guánica after implementing management actions, suggesting that the impact of the hurricanes on water clarity probably outweighed the efforts made in the watershed to improve water quality. Further analysis should be done as new data is available and after the implementation of the last management actions suggested in the plan.

Understanding the Sun’s long-term radiative variability is essential for assessing its impact on the Earth’s upper atmosphere and long-term climate. In particular, solar extreme ultraviolet (EUV) irradiance plays a central role in heating the ionosphere, thermosphere, and mesosphere. However, continuous EUV flux measurements have only been available since 1995, leaving substantial historical gaps that constrain investigations of solar influences across multiple solar cycles. We present a Bayesian deep learning framework, SEMNet, developed to reconstruct historical solar EUV irradiance from CaII K images. Trained on SOHO/SEM EUV fluxes from 1998 to 2014 and paired with CaII K observations from the Precision Solar Photometric Telescope, SEMNet produces accurate reconstructions with associated uncertainty estimates. The model is further extended via transfer learning to recover EUV fluxes back to 1907 using historical CaII K data from the Kodaikanal Solar Observatory. Results show that SEMNet generates consistent and physically meaningful irradiance estimates, demonstrating that CaII K imaging can serve as a reliable long-term proxy. This approach offers a data-driven solution for filling gaps in solar irradiance records and enhances our ability to study solar influences on the Earth’s atmosphere and climate over more than a century.

Satellite-based soil moisture retrieval (SSMR) is crucial for various applications such as crop yield estimation, land surface-climate interactions, drought monitoring, irrigation management, and urban water management. SSMR covers two key areas: surface soil moisture (SSM) analysis, which focuses on the top 3–5 cm of soil, and root-zone soil moisture (RZSM) estimation, which extends to depths of 30–110 cm. While remote sensing has been widely utilized for large-scale SSM estimation, its application for farm-scale RZSM retrieval and soil hydraulic parameter estimation remains underexplored and previous studies have primarily assimilated either land surface temperature (LST) or SSM separately. This research, thus, emphasizes the interdependency between soil moisture and temperature and proposes a new method using a reduced-order variational approach to simultaneously assimilate LST and SSM data from thermal and microwave satellite remote sensing into the HYDRUS-1D model. Our framework aims to estimate soil hydraulic parameters and RZSM profiles at the farm scale (~30m-1km) using data from MODIS, LANDSAT-8, and SMAP, by combining the horizontal coverage and spatial resolution of remote sensing with the vertical coverage and temporal continuity of the soil moisture simulation models. We have designed an Observing System Simulation Experiment (OSSE) to evaluate this framework. Preliminary results indicate that coupling soil temperature and moisture models enhances the accuracy of surface energy and water flux predictions. Additionally, it enables more precise estimation of soil hydraulic parameters compared to models that assimilate only LST or SSM data. These findings highlight the potential of using freely available satellite data to estimate RZSM profiles and soil hydraulic parameters globally, thereby reducing the reliance on costly ground-based measurements. This research advances hydrological modeling capabilities and demonstrates how integrated satellite observations can improve water resource management, particularly in underserved communities.

The El Niño Southern Oscillation (ENSO) is a key driver of global climate variability. Through its teleconnections, ENSO influences precipitation and temperature extremes worldwide, yet its influence on compound extremes across agricultural systems is less well explored. Here, we examine ENSO's influence on co-occurring hot-dry (HD) and hot-wet (HW) events across global croplands, given their distinct risks for food production. HD events intensify crop stress by combining heat and moisture deficits, while HW events can increase risks of pests, pathogens, and post-harvest losses. Using the Niño 3.4 index, high-resolution climate data (1901–2024), and crop extent and calendar data, we quantify the localized risk of these compound heat events and assess exposure for all croplands as well as staple crops of maize, rice, soybeans, and wheat. We show increased risk of both HD and HW events across global croplands during  El Niños. For example, the localized risk of HD events more than doubles across parts of global croplands including India, Australia, the Sahel, Brazil and Mexico during El Niño relative to ENSO-Neutral years, resulting in a significantly elevated global cropland exposure to HD events during developing El Niño summers. Localized risk of HW events also more than doubles particularly across India, Argentina, Brazil, and parts of Sahel with significantly elevated cropland exposure in decaying El Niño summers. Of the four staple crops, rice shows the strongest link to ENSO, with global exposure increasing > 30% and > 40% above Neutral years for HD and HW, respectively. These findings demonstrate that ENSO modifies multi-crop risks through compound events, highlighting the need to integrate ENSO risks for compound heat events into early warning systems and country- and crop-specific adaptation strategies to safeguard food security in vulnerable regions.

Green plants play a vital role in mitigating urban air pollution, and selecting pollution-tolerant species is essential for sustainable greenbelt development. This study evaluates 12 dominant roadside plant species from National Highway 16 (NH-16) at Kharagpur, India, using two indices: the Air Pollution Tolerance Index (APTI) and the Anticipated Performance Index (API). Four biochemical parameters such as relative water content (RWC), leaf extract pH, ascorbic acid (AA), and total chlorophyll (TCH) were used to calculate APTI. The IIT Kharagpur campus was considered the control site to compare exposure levels and corresponding changes in plant species between the control (campus site) and polluted (roadside) environments. Duranta erecta and Neolamarckia cadamba demonstrated strong performance in APTI and API on both environments, whereas Cascabela thevetia, Albizia lebbeck etc. scored poorly. Interestingly, most species exhibited higher APTI values at the road site (mean=23.71) compared to the control site (mean=18.11), suggesting enhanced internal defence mechanisms in response to pollution stress. A multiple linear regression (MLR) model identified AA as the most influential significant predictor of APTI (β = 1.002, p<0.001), followed by pH, TCH, and RWC (β = 0.114, 0.100 and 0.077, respectively). There was no multicollinearity among the selected parameters, as all VIF values were below 2. In addition to biochemical traits, key morphological features such as stomatal density and cuticle thickness were examined as adaptive indicators that showed increased values under polluted conditions, suggesting physiological adaptation. Moreover, Duranta erecta exhibited the highest increase in stomatal density (58.95%), while Sasmanea saman showed the greatest increase in cuticle thickness (59.35%), reinforcing their suitability as roadside species for long-term air pollution abatement. Therefore, the integrated assessment of physiological, biochemical, and morphological traits of the selected dominant species highlight that the study outcomes can be used to select plant species for greening initiatives along National Highway corridors.Keywords: APTI; API; Multiple linear regression; Stomatal density

Scientific communities are straining under mounting challenges—knowledge fragmentation, outdated publication processes, institutional erosion and funding cuts—that threaten our capacity to address urgent global problems. Traditional scientific processes and infrastructure are driven by misaligned incentives that reward competitive publications over collaborative knowledge-building.

We present a three-dimensional numerical model of tectonic plates that self-consistently develop in the convecting mantle. The viscosity depends on stress-history as well as temperature. The lithosphere develops as the upper highly viscous part of the thermal boundary layer along the surface boundary owing to a strong dependence of the viscosity on temperature. When the stress S exceeds a threshold Sp in the lithosphere, however, we assumed that the viscosity drops by orders of magnitude and keeps the low value, even when S is reduced below Sp, provided that S remains higher than the other threshold Sm (< Sp). Sp corresponds to the rupture strength of the tectonic plates, while Sm to that at plate margins. The viscosity is a two-valued function of the stress S in the range of (Sm, Sp), and which of the two values the lithosphere chooses is determined by whether or not S has exceeded Sp in the past. When the model parameters are tuned so that the stress in the lithosphere stays in the range, the lithosphere is rifted into several highly viscous pieces, or tectonic plates, separated by narrow plate margins where the viscosity takes the lower value, and the tectonic plates rigidly and stably move and occasionally rotate for several hundred million years or longer. (See the planform of the plate motion shown in the figure. The arrows show the velocity, while the color shows the relative viscosity on the surface.) Because of the rather stable plate motion, the heat flow decreases with the distance from the spreading centers L in their vicinity as 1/sqrt(L), and the secondary convection occurs beneath the plates in the form of two-dimensional rolls with their axes aligned with the direction of plate motions.

As the climate warms and the transition from a perennial to a seasonal Arctic sea-ice cover becomes imminent, knowledge of melt ponding is central to understanding changes in the new Arctic. The Density Dimension Algorithm for Bifurcating Sea-Ice Reflectors (DDA-bifurcate-seaice), applied to NASA’s Ice, Cloud and land Elevation (ICESat-2) photon data, has the ability to provide measurements and monitoring of the onset of melt in the Arctic and on melt progression through its ability to detect multiple surfaces within the photon cloud. The DDA-bifurcate-seaice detects and characterizes melt ponds by retrieving two surface heights, pond surface and bottom, and measurements of depth and width of melt ponds at near sensor resolution. Ponds are generally missed in the standard ICESat-2 Sea Ice Height data product (ATL07) due to its lower resolution and inability to detect multiple surfaces.

The dynamic equilibrium of a river basin can be modified by both natural as well as anthropogenic factors. Brahmani River Basin, bounded by the Mahanadi Rift and Chotanagpur plateau in the Indian shield, is the one of the largest river systems in India. In this study, various geomorphic properties of the 280 sub-watersheds of the Brahmani River basin have been investigated using ArcGIS and Matlab. This study aims to quantify evolutionary traits by investigating detailed geomorphic indices. Aside from the indices, longitudinal profiles and the channel steepness index (ksn) and knickpoint analysis have been evaluated to examine the river’s response to lithologic control and their relationship to the underlying reactivated structural features. Additionally, disequilibrium and transience dynamics have been measured, which revealed that most sub-basins are tectonically enhanced, naturally extended, and structurally organized. Besides this, reconstruction of paleo-relief of rivers longitudinal profiles have been done. Further, evidence also supports that anthropogenic stresses have been responsible for sediment transport during the Anthropocene. Statistical analysis such as the Mann-Kendall test, Pettitt test, Sen’s Slope, and double mass curve of recent suspended sediment load (1980 to 2021) at the terminal station also helps to detect the sudden change point. Collectively, the findings highlight a landscape intricately modified by both natural forces and human activities, underscoring the importance of integrated management and conservation strategies in response to these influences. Keywords: Geomorphic Indices, Sediment load, Lithology, Topotoolbox, Peninsular India.

Skillful subseasonal-to-seasonal (S2S) forecasts rely heavily on windows of opportunity, often afforded by teleconnected sources of variability throughout the Earth system. Due to its longer decorrelation scale, the stratosphere is highlighted as one such source of variability. Recently, the surface impacts of stratospheric variability on S2S timescales have been viewed not only through the mean flow diagnostics but also through the distinct geometry and behavior of the stratospheric polar vortex in the wintertime (November through March). This analysis will assess the impact of stratospheric polar vortex variability on the troposphere through its interactions with climate modes, primarily the North Atlantic Oscillation (NAO).  In this research, lower stratospheric potential vorticity (PV) that aids in capturing troposphere-stratosphere coupling processes is used to investigate stratospheric sources of S2S prediction skill. The analysis uses ERA-5 reanalysis data to assess stratospheric teleconnections to the NAO index as described by geopotential height (GPH) anomalies and their contributions to S2S predictions of 2-meter wintertime temperature anomalies across the Northern Hemisphere. Using eXplainable Artificial Intelligence (XAI) and Machine Learning, this research builds dynamical-statistical probabilistic forecasts of 2-meter temperature anomaly on the S2S scale over Eurasia through a feed-forward Artificial Neural Network (ANN) framework. The merged ANN model is constructed with data that includes 14-day lead lower stratospheric PV at 100hPa and synoptic scale North Atlantic GPH anomalies at 500hPa. The model output is interpreted through an XAI method called Layerwise Relevance Propagation (LRP), which is used to examine how each input feature influences the 2-meter temperature predictions and highlights their relative importance. Requisite spatial maps of the input features discretely connect physical processes from the stratosphere to the NAO and highlight potential windows of opportunity for confident and correct predictions of  2-meter temperature outcomes on the S2S timescale.

CRISPI (Compositional Regolith and Icy Surface analysis via Particle Impact) is a proposed ridealong in-situ mass spectrometer (MS) smallsat mission to provide synergistic astrobiology science at Ariel to the Uranus Orbiter and Probe (UOP) mission. By utilizing a high-resolution impact ionization MS based on the Europa Clipper’s Surface Dust Analyzer on a deployable smallsat platform, CRISPSI will provide in-situ mass data of the surface of Ariel, a moon of Uranus and one of the highest-priority astrobiology targets in the outer solar system. Ariel is specifically called out in the Origins, Worlds, and Life (OWL) Decadal Survey as a high-priority target because it is potentially a liquid-water-bearing icy ocean world that is known to have ammonia, an organic volatile species that indicates recent surface processing (possibly from cryovolcanism or other subsurface sources) and may act as an anti-freeze for water. However, the baseline UOP mission has few flybys of Ariel with limited compositional data provided only by optical spectrometers. CRISPI addresses this gap by enabling the direct and unambiguous detection of organics and salts present in and the quantification of ice-to-rock and salt-to-water ratios of >1000 surface dust grains mapped to visible features on Ariel’s surface. In principle, CRISPI may also be used to study Miranda (another high priority astrobiology target) and the Uranus ring systems, which are poorly understood and are also specifically called out in the OWL Decadal Survey. Here we present results from the Cornell SmallSat Mission Design School (SMDS) that developed the CRISPI mission concept. We propose CRISPI to work in synergy with the UOP’s magnetometer, imagers, and optical spectrometers to extend its science reach, break ambiguities in optical spectrometer data, and provide unique and critical in-situ mass spectral data to assess Ariel’s habitability and search for organic signs of past or extant life.

Tree height estimation is crucial for accurate biomass estimation and forest monitoring. Synthetic Aperture Radar (SAR) offers two products useful for tree height estimation: SLC (Single Look Complex) images and derived tomographic cubes. In this work, we present machine learning methods to predict forest height in two ways, directly from SLC images and from tomographic cubes, providing valuable context to the upcoming European Space Agency’s Biomass Satellite mission.

In agricultural science, researchers face difficulties publishing and sharing their data-driven crop yield forecast models. The lack of direct and public access to the resulting models impedes collaboration among stakeholders, obscures relevant context and hinders continuous use of the contributions. In addition, researchers have to spend a significant amount of time on accessing, aggregating and pre-processing feature data from various sources. To address both issues, we present a platform that enables (i) public access to standardized and high-quality data by providing (ii) a uniform interface and SDK for data retrieval, as well as (iii) seamless hosting of data-driven crop yield prediction models. Our platform standardizes datasets, sourced from canonical providers such as Copernicus and ISRIC in the areas of observed and forecast weather, soil, and vegetation indices. We facilitate access to this data at different spatial and temporal resolutions through a uniform interface. Thereby, researchers can immediately focus on model development without having to manually gather data from heterogeneous sources. We further allow researchers to directly upload and deploy their crop yield prediction models by submitting their own code written in the Python programming language. The upload process is flexible, minimizing the integration effort on the researcher’s side. The uploaded models are deployed on the platform; historical as well as future prediction values are computed automatically and continuously. The predictions are visualized in an intuitive way, enabling direct comparison to other models and actual historical yield values as well as a direct assessment of models’ forecast capabilities. By publishing their models on our platform, researchers can increase their work’s visibility to actors from academia, media, politics, and business. This enables direct communication between the scientific community and the public, cultivating engagement, feedback and knowledge sharing. In summary, our platform revolutionizes the development and deployment of yield forecast models by providing a comprehensive suite of standardized data sets and the functionality for direct deployment of researchers’ models. This makes yield modeling seamless, collaborative and visible.

Classically, routing in Land Surface Models (LSMs) has been achieved using simple schemes with relatively coarse spatial resolution, such the 0.5-degree TRIP model and its derivatives. However, moving to higher resolution requires us to re-think this approach, but at the expense of increasing complexity and computational cost. Some clues to what these new methodologies might be can potentially be found in the field of global flood inundation modelling. Here, over the last ten years, a set of methods have been developed to map areas at risk of flood inundation at high (now 1 arcsecond) resolution over the entire land surface using full 2D hydrodynamic methods. Such approaches are likely still too costly to implement directly in Land Surface Models, but a consideration of findings from these studies can indicate the essential elements that would need to be included in more a lightweight approach.

Accurate quantification of uncertainty and error in flood prediction is crucial for informed decision-making process. This study investigates the application of various uncertainty quantification approaches for flood prediction using state-of-the-art neural network models. By incorporating techniques such as Bayesian inference, Monte Carlo approach, and quantile regression, we aim to provide a comprehensive assessment of predictive uncertainty for flood prediction across multiple timescales. Our results highlight the comparative effectiveness of these models under different flood magnitudes, emphasizing their strengths and limitations across scales. This research contributes to improving the reliability of flood prediction models, thereby enhancing decision-making processes for water resource management and disaster preparedness. We expect that the findings will offer valuable insights into the integration of advanced deep learning techniques with uncertainty quantification to reduce error in short-long-term flood predictions.

The Atlantic Coastal Plain is comprised of thick, unconsolidated sediments that span much of the East Coast of the United States. Seismic waves passing through this shallow, loosely compacted layer of sedimentary rock display increased ground motion amplitudes as measured on broadband seismometers deployed during the Southeastern Suture of the Appalachian Margin Experiment (SESAME) from 2010–2014. This amplification can lead to greater damage during earthquakes, making site response analysis, and constraints on the associated shallow shear velocity structure, important for informing ground motion modeling in the region. Site amplification is most noticeable in the horizontal direction and, therefore, can be quantified at single seismic stations by dividing the horizontal spectrum by the vertical spectrum of ambient seismic noise. The resulting horizontal-to-vertical spectral ratio (HVSR) provides a simple estimate of the fundamental frequency of a given site; however, more holistic modeling of the complete HVSR curve to constrain shallow elastic structure remains challenging. We leverage the diffuse field assumption (DFA) to interpret HVSR in terms of the ratios of the imaginary parts of the Green’s function components, which allows for the inversion of HVSR curves for elastic structure. We measure HVSR at 91 broadband stations of the Southeastern Suture of the Appalachian Margin Experiment (SESAME) and apply the HV-Inv tool to model the Atlantic Coastal Plain sediment structure beneath each station. While we observe clear lateral variability in HVSR across the array due to variations in sediment structure, we generally find little azimuthal variability of HVSR at most stations, allowing local simplification of the structure to horizontal layers. To resolve tradeoffs in the elastic structure due to fitting HVSR alone, we plan to incorporate additional observations of high-frequency Rayleigh wave ellipticity and multimode phase velocity estimated from ambient noise cross-correlations. Together, these complementary datasets will contribute to a robust framework for subsurface characterization in complex geological settings.

Climate change is expected to deeply affect the water resources of Texas; however, climate change is not recognized at the state-level as a concern. Without recognition, state resources cannot support the research and publication of data, from a variety of scientific and cultural perspectives, which inform stakeholders and leads to the development of effective mitigation strategies. To mind this gap of information, we need to learn how decisionmakers consider climate change impacts on water resources; however, not all decisionmakers in Texas recognize climate change. This study surveyed a specialized population of professionals working at regional water planning groups, groundwater conservation districts, river authorities, water suppliers and wholesalers, and water utilities in Texas to gain knowledge about how these stakeholders, which play an active role in shaping and implementing water resources policy, perceive climate change and the resilience of the water resources under the care of their organizations. These high-level positions require specialized knowledge and expose professionals to different experiences with water resources which may promote a difference in the perceptions of climate change between this population and the public. The survey included two primary paths—one for those who acknowledged climate change and one for those who did not. The results indicate that the dynamic of perceptions on climate change from this specialized population of professional Texans, operating within a restrictive political culture, are not so different from those expressed by the public at national and state scales; however, there is evidence that the weight of cultural elements exceeds the weight of a multitude of parameters—including education, professional experience, and belief in climate change. There are variances in attribution of cause even within those who recognize climate change and an expression of more significant concern about the resiliency of water resources from those who do not recognize climate change. The results indicate that perceptions, attitudes, and actions around climate change and the advancement of the resiliency of water resources are much more complex than researchers are imagining.

Liquid water content (LWC) in seasonal snowpacks is a critical state variable influencing snowmelt runoff, flood forecasting, avalanche prediction, and radar remote sensing. However, LWC observations are not commonly measured, leading to a dependence on model estimation. The representation of LWC in process-based snow models varies widely, with differences in how meltwater retention, percolation, and refreezing are handled, ranging from simple bucket schemes to physically-based formulations using Darcy’s law or Richards’ equation. Despite its significance, the simulation of LWC across physics-based models remains understudied, particularly within data assimilation (DA) systems. Leveraging the physically-consistency of a model, data assimilation of one type of snow observation (snow depth) has been shown to improve other unobserved states (like snow density), but the improvements to LWC have not been assessed. In this study, we test the impact of assimilating snow depth and snow disappearance date (SDD) on LWC estimates using Flexible now Model (FSM2). The model is forced with local meteorological observations and snow characteristics are compared before (reference) and after assimilation.We validate the results using in situ LWC measurements and also explore how changes in FSM2’s process complexity affect performance. We hypothesize that data assimilation will improve the LWC estimation and hence, this research will address the key gaps related to LWC uncertainty and the value of model complexity, offering insights for improving LWC retrievals in the absence of remotely sensed LWC data.