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Climate change is coming… but what on Earth can we do about it? Scientist Dr. Kimberley Miner has written a guide to riding out the oncoming almighty storm.

A new study from UNC-Chapel Hill reveals that repetitive flooding in North Carolina is far more common and more widespread than previously recognized, with over 20,000 buildings flooding multiple times between 1996 and 2020.

Source: Journal of Geophysical Research: Biogeosciences

When sunlight hits clouds or other atmospheric particles, it scatters and becomes diffuse light. Unlike direct sunlight, diffuse light can reach deeper into shaded plant canopies, where plants have dense, layered leaves. The diffuse-light fertilization effect theory suggests that diffuse light in such environments can promote carbon uptake and influence canopy temperature and evapotranspiration. Prior research suggests that some diffuse light can also boost photosynthesis, but after an optimal point, the overall reduction in total radiation will decrease photosynthesis.

However, diffuse light is not typically measured at ground-based sites. Previous studies used indirect methods to infer its effects on plants, including running computer models and measuring atmospheric properties such as clearness. So questions remained about the optimal amount of this filtered sunlight for vegetation.

Since 2017, the National Ecological Observatory Network (NEON) has collected data on diffuse sunlight, evapotranspiration, and other ecological variables across 32 sites in the continental United States, including forests, grasslands, shrubs, and cultivated crops. Schwartz et al. used the NEON dataset combined with satellite records from the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) to determine how diffuse sunlight connects to evapotranspiration and net ecosystem exchange, or the carbon exchange between ecosystems and the atmosphere.

Their findings suggested that across NEON sites between 2018 and 2022, evapotranspiration decreased as diffuse radiation increased, and no optimal point was observed, contrary to what previous modeling suggested. Evapotranspiration, the researchers found, may be more strongly affected by available moisture than by either direct or diffuse light.

However, diffuse sunlight did enhance net ecosystem exchange in some locations, including forests and areas with shrub or scrub vegetation. Nineteen of the 32 sites showed a positive net ecosystem exchange response to diffuse light, meaning that more carbon can be absorbed when sunlight is scattered. (Journal of Geophysical Research: Biogeosciences, https://doi.org/10.1029/2025JG008757, 2025)

—Rebecca Owen (@beccapox.bsky.social), Science Writer

Citation: Owen, R. (2025), How plants respond to scattered sunlight, Eos, 106, https://doi.org/10.1029/2025EO250249. Published on 14 July 2025. Text © 2025. AGU. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

Shoppers may use a plastic bag for only a few minutes before tossing it in the trash. Inefficient waste disposal, however, may allow that bag to find its way into streams and, ultimately, coastal ecosystems. There, plastic pollution can imperil marine plants and animals as well as the economic value of beachfront businesses.

“Plastic bags are designed to be single use. They’re designed to be lightweight. Even if we’re trying to properly manage them, they just get into the environment more easily than other plastics,” said Erin Murphy, ocean plastics science and research manager at the Ocean Conservancy.

“These [plastic bag bans] are effective policies, regardless of the scale of governance in which you implement them.”

While many states and municipalities have plastic bag bans or require fees for customers who want a bag, there is no national policy that aims to reduce the number of plastic bags used in the United States.

But a study published last month in Science shows some promising results: In places with bag bans and fees, the number of plastic bags found on local beaches and shorelines has dropped significantly.

“A lot of the time, communities don’t feel like they can implement policy that will directly impact their communities and directly benefit their communities. This study showed that whether it’s a town or state, these [plastic bag bans] are effective policies, regardless of the scale of governance in which you implement them,” said Murphy, who was not involved in the research.

Analyzing the Trash

Study authors Anna Papp and Kimberly Oremus examined data collected from 45,067 shoreline cleanup events between 2016 and 2023. During these events, organized by the Ocean Conservancy, participants collected trash along a beach and logged their findings into the Trash Information and Data for Education and Solutions (TIDES) database.

Plastic bags are the fifth most common item found during these shoreline cleanups, making up 4.5% of all cataloged trash. (Some of the more unusual items logged include golf balls, Mardi Gras beads, and fake nails.)

Papp and Oremus cross-checked the cleanup data with 182 plastic bag policies around the United States that were enacted between 2017 and 2023. The discrepancy between the dates of the cleanup data (starting in 2016) and the policy data (starting a year later) allowed the researchers to use the 2016 data as a control to evaluate how trends in plastic bag litter may have changed in response to local or state-level regulation.

“Comprehensive data on plastics in the environment can be challenging to find, so the cleanup data offered a new way of measuring plastic bag litter in the environment. This, combined with the wide reach of bag policies in the U.S. in recent years, made our study possible,” said Papp, an environmental economist at the Massachusetts Institute of Technology.

A Broad Spectrum of Bans

Across the country, a hodgepodge of legislation exists to manage plastic bag waste, from strict bans (like the ones implemented in New Jersey, where single-use paper bags are also limited), to partial bans (like the ones in California, targeted at large retailers), to required fees (as in Oregon, where retailers must charge at least 5 cents for a thick, presumably reusable plastic bag). In addition to statewide legislation, hundreds of municipalities have their own plastic bag policies.

“During our data collection phase, I was initially surprised by the reach of plastic bag policies. We estimate that now one in every three Americans lives in an area with some bag policy,” said Papp.

Papp and Oremus were able to document the effectiveness of such policies, regardless of their reach. In places where some form of plastic bag legislation exists, data showed a 25%–47% decrease in the proportion of plastic bags recovered in coastline cleanups. Although all policies aimed at reducing plastic bag litter were effective, researchers found that those implemented at the state level correlated most strongly to reducing the amount of plastic bag waste found during beach cleanups.

“In some ways it’s like, well, of course, if you use fewer plastic bags, you’re going to find fewer plastic bags on the beach, but it’s good that [researchers] documented that in a quantitative way,” said Susanne Brander, an ecotoxicologist at Oregon State University who was not involved in the study. “We need those data in order to convince additional lawmakers and agencies to take this seriously and to think not just about plastic bags, but about other single-use items as well.”

—Rebecca Owen (@beccapox.bsky.social), Science Writer

Citation: Owen, R. (2025), Policy success: Fees and bans on plastic bags reduce beach trash, Eos, 106, https://doi.org/10.1029/2025EO250247. Published on 14 July 2025. Text © 2025. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

Tropical cyclones hundreds of kilometers away from the Philippines are often more responsible for heavy rainfall than those that hit the country directly during the annual "Habagat" or southwest monsoon season from July to September, according to new research published in Atmospheric Research.

The cleanup of air pollution in East Asia has accelerated global warming, a new study published today (Monday, 14 July) in the journal Communications Earth and Environment has found.

A new paper (Dalal et al. 2025) in the journal Engineering Geology examines movement of a major landslide complex in India. It shows that the slope reactivated in 2018, probably as a result of the loss of vegetation and poor management of water.

Loyal readers will remember a series of posts that I made back in early 2023 regarding accelerated movement of the complex landslide system located beneath the town of Joshimath located in the Himalaya in Uttarakhand, India. At this time, there was a significant increase in the movement rate of the landslide, causing substantial damage to structures within the town.

Joshimath is located at [30.5526, 79.5628]. This is a Google Earth image of the town in 2022. The complex landslides in the area are quite easy to see:-

Google Earth image of the town of Joshimath in northern India.

A very nice paper (Dalal et al. 2025) has been published in the journal Engineering Geology. The authors have used InSAR to examine the long term movement pattern of the landslides – the InSAR data extends back to 2017. In it, they demonstrate that the slope did indeed undergo a phase of rapid movement in early 2023, and they link this to heavy rainfall that occurred in October 2022, which increased the pore water pressure in the slope.

But there are some interesting details in this piece of work. First, the slope actually started to move in 2018, and showed a seasonal pattern of deformation until the rapid movement even in 2023. The authors link this reactivation of the landslide at Joshimath to progressive urbanisation and removal of the vegetation canopy – modelling indicates that the factor of safety of the slope has been notably reduced by this effect. This is quite surprising as the failure at Joshimath is deep-seated, where vegetation does not normall play a major role.

Second, the analysis also highlights that “mismanaged groundwater seepage and blocked drainage paths further exacerbated slope weakening.” This is a common problem in rapidly developing Himalayan communities.

Finally, and most worryingly, Dalal et al. (2025) indicate that the slope could be undergoing progressive failure towards a catastrophic collapse. They have modelled runout scenarios for the slope, which indicate that such an event would threaten the Tapovan Vishnugad hydropower project downstream.

All of this indicates that action is needed at Joshimath. If a large-scale mitigation project is not possible (and I recognise that this would be extremely expensive and very challenging), efforts should be made to manage water (and drainage) across the whole area, and the slope should be monitored in real time.

Reference

Dala, P. et al. 2025. Deformation dynamics and hazard of slow-moving landslides: The 2023 Joshimath event, Uttarakhand Himalaya. Engineering Geology, 354, 108201. Doi: https://doi.org/10.1016/j.enggeo.2025.108201

Return to The Landslide Blog homepage Text © 2023. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

SummaryThis paper investigates the seismic activity and velocity structure in the Three Gorges Reservoir (TGR) region using high-quality travel time data from an extensive seismic observation network. The primary goal is to understand the relationship between the three-dimensional velocity structure and seismicity within the reservoir area. We employed advanced inversion techniques to develop detailed 3-D models of the P- and S-wave velocities and analyzed the focal mechanisms of significant seismic events. Our results reveal that there are substantial lateral variations in the upper crustal velocity structure, with high-velocity zones in the northeastern region of Badong and lower velocities in the Zigui Basin (ZGB). The sedimentary layers in the ZGB are 6–8 km thick, and low S-wave velocity anomalies extend from this depth and are correlated with the Triassic formations. The seismic activity patterns show that the earthquakes in the Badong region were concentrated along three east–west trending belts within the core of an anticline. These patterns suggest that the geological structures and fluid infiltration significantly influence the seismicity. In particular, the M5.1 Badong earthquake occurred at the boundary of a high-velocity zone and was associated with a seismic belt extending from shallow to deeper depths. The results of this study highlight the complex interactions between rock heterogeneity, fault dynamics, and fluid effects, providing a comprehensive analysis of reservoir-induced seismicity. This work provides a better understanding of the physical mechanisms driving seismic activity in large reservoir systems and provides insights relevant to seismic hazard assessment and reservoir management.

Abstract

To meet the high-precision and high-integrity positioning demands of safety–critical applications, monitoring the quality of precise satellite products in global navigation satellite system (GNSS) precise point positioning (PPP) is crucial. This work employs ionosphere-free (IF) PPP with ambiguity resolution (PPP-AR) phase residuals to construct test statistics for monitoring the quality of precise satellite corrections. By utilizing precise satellite orbit and clock products from CODE, WUM, and GRG, the PPP-AR phase residuals were first analyzed with sample moments, Allan variance and power spectral density (PSD). The key findings are as follows: (1) The skewness and kurtosis results indicate that ambiguity-fixed phase residuals deviate from an ideal zero-mean Gaussian distribution and exhibit a super-Gaussian distribution. (2) Allan variance and PSD analysis reveal that flicker noise dominates the phase residuals. (3) The noise amplitudes are similar for all satellites, but certain differences are observed among different GNSS systems and satellite types. (4) The noise level of phase residuals is influenced by the receiver types, antenna types, and precise products from different analysis centers. Leveraging the error characteristics, the two-step Gaussian overbounding (OB) method was employed to estimate the corresponding OB parameters of the phase residuals. The overbounding results demonstrate that, under similar conditions, phase residuals can be bounded by the calculated bound within the acceptable integrity risk after removing the detected outliers. Anomaly monitoring experiments further show that phase residuals can effectively capture anomalies in precise satellite corrections, with the set threshold successfully detecting such anomalies.

The measurement of virtual height of the sporadic E layer (h'Es) is very sensitive to the type of ionosonde used and the calibration processes. The ionosondes used by the national institute of communication an...

Abstract

Solar photospheric abundances and CI-chondrite compositions are reviewed and updated to obtain representative solar system abundances of the elements and their isotopes. The new photospheric abundances obtained here lead to higher solar metallicity. Full 3D NLTE photospheric analyses are only available for 11 elements. A quality index for analyses is introduced. For several elements, uncertainties remain large. Protosolar mass fractions are H (X = 0.7060), He (Y = 0.2753), and for metals Li to U (Z = 0.0187). The protosolar (C+N)/H agrees within 13% with the ratio for the solar core from the Borexino experiment. Elemental abundances in CI-chondrites were screened by analytical methods, sample sizes, and evaluated using concentration frequency distributions. Aqueously mobile elements (e.g., alkalis, alkaline earths, etc.) often deviate from normal distributions indicating mobilization and/or sequestration into carbonates, phosphates, and sulfates. Revised CI-chondrite abundances of non-volatile elements are similar to earlier estimates. The moderately volatile elements F and Sb are higher than before, as are C, Br and I, whereas the CI-abundances of Hg and N are now significantly lower. The solar system nuclide distribution curves of s-process elements agree within 4% with s-process predictions of Galactic chemical evolution models. P-process nuclide distributions are assessed. No obvious correlation of CI-chondritic to solar elemental abundance ratios with condensation temperatures is observed, nor is there one for ratios of CI-chondrites/solar wind abundances.

The January 2022 Tonga volcanic eruption generated atmospheric pressure waves that propagated over the ocean’s surface and triggered a meteotsunami. This meteotsunami caused significant amplitudes exceeding 10...

Abstract

Accurately modeling and prediction the nonlinear motion of GNSS (Global Navigation Satellite System) coordinate time series holds significant theoretical and practical value for the study of geodynamics. A novel integrated network, named Ensemble Learning method based on Signal Source Driver (ELSSD), is proposed, which leverages the strengths of Long Short-Term Memory (LSTM) and Deep Self-Attention Neural Network (DSANN), while integrating GNSS loading data as an additional data source. Additionally, a multi-track synchronous sliding window data processing strategy is designed to address the challenge of multi-source data fusion input. The effectiveness of this algorithm is validated using GNSS coordinate time series from 186 global stations over a period of 10 years. Experimental results initially illustrate that, when accounting for displacement caused by environmental loading effects, there is a marked improvement in the modeling and prediction accuracy compared with GNSS input-only. Furthermore, the application of three ensemble network strategies-Bagging, Boosting, and Stacking-have further been demonstrated to enhance modeling and prediction accuracy. Compared with LSTM and DSANN networks, the proposed ELSSD algorithm achieves an average RMSE (Root Mean Square Error) of 3.6 mm for both modeling and prediction, with modeling accuracy improvements of 4.8% and 6.2%, while prediction accuracy improvements of 5.4% and 5.9%, respectively. With respect to the traditional Least Square method, there is an improvement of 22.1% and 27.9% in modeling and prediction accuracy, respectively. Regarding noise characteristics, there is a significant reduction in colored noise amplitude, with decreases of 36.7% and 36.0% observed in modeling and prediction, respectively. Simultaneously, the velocity uncertainty experiences an average reduction of 27.1% and 27.5%. The average velocity differences are measured at 0.06 mm/year and 0.24 mm/year, respectively. Hence, our findings suggest that the ELSSD algorithm emerges as an effective methodology for handling multi-source data input in GNSS coordinate time series, presenting promising practical applications in the field.

The Mw 7.5 Noto Peninsula earthquake, which occurred on January 1, 2024, was considerably hazardous to the peninsula and surrounding regions owing to a strong motion, large-scale crustal deformation, and subse...

Nature Geoscience, Published online: 21 February 2025; doi:10.1038/s41561-025-01653-z

Quartz-rich clasts in Martian meteorite NWA 7533 indicate the presence of granitic rocks on early Mars that formed via hydrothermal activity and impact melting, according to petrologic and in situ geochemical analyses.

Abstract

The predictive learning of total electron content (TEC) spatiotemporal sequences aims to generate future TEC maps by learning from historical data, where both the spatial appearances and temporal variations are crucial for accurate predictions. However, the state-of-the-art TEC map prediction models typically employ sequential stacking of ConvLSTM, ConvGRU, and their variants. These models focus more on modeling temporal variations, and the spatial features extracted from the historical sequence are highly abstracted, resulting in the fine-grained spatial appearances not being adequately memorized or transmitted, leading to fuzzy prediction results during storm time. In this paper, we used PredRNN to propose a storm-time ionospheric TEC spatiotemporal prediction model with multichannel features, named Multichannel PredRNN, which can simultaneously remember the temporal patterns and spatial appearances in input sequence. The temporal memory as well as the spatial memory are updated repeatedly over time, ensuring that both temporal memory and spatiotemporal memory are fully utilized in prediction. According to Dst index, 60 magnetic storm events from 2011 to 2019 were selected as the dataset. We first discussed the impact of feature combinations on predictive performance. The results show that using multichannel feature (TEC + Dst&F10.7), the Multichannel PredRNN and the comparison models ConvGRU and ConvLSTM have the best prediction performance. Then we used the optimal feature combination for prediction. We compared Multichannel PredRNN with IRI-2016, COPG, ConvLSTM and ConvGRU under various conditions, including the entire test magnetic events, periods of quiet and storm, different phases of geomagnetic storms, and the most severe geomagnetic storms. Finally, we compared the performance of different output steps. The experimental results indicate that in all cases, Multichannel PredRNN with dual memory state and zigzag flow is superior to four compared models.

Abstract

The gravimetry measurements from the Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) mission provide an essential way to monitor changes in ocean bottom pressure ( \(p_b\) ), which is a critical variable in understanding ocean circulation. However, the coarse spatial resolution of the GRACE(-FO) fields blurs important spatial details, such as \(p_b\) gradients. In this study, we employ a self-supervised deep learning algorithm to downscale global monthly \(p_b\) anomalies derived from GRACE(-FO) observations to an equal-angle 0.25  \( ^{\circ }\) grid in the absence of high-resolution ground truth. The optimization process is realized by constraining the outputs to follow the large-scale mass conservation contained in the gravity field estimates while learning the spatial details from two ocean reanalysis products. The downscaled product agrees with GRACE(-FO) solutions over large ocean basins at the millimeter level in terms of equivalent water height and shows signs of outperforming them when evaluating short spatial scale variability. In particular, the downscaled \(p_b\) product has more realistic signal content near the coast and exhibits better agreement with tide gauge measurements at around 80% of 465 globally distributed stations. Our method presents a novel way of combining the advantages of satellite measurements and ocean models at the product level, with potential downstream applications for studies of the large-scale ocean circulation, coastal sea level variability, and changes in global geodetic parameters.

Abstract

Data-driven technologies have shown promising potential for improving GNSS positioning, which can analyze observation data to learn the complex hidden characteristics of system models, without rigorous prior assumptions. However, in complex urban areas, the input observation data contain task-irrelevant noisy GNSS measurements arising from stochastic noise, such as signal reflections from tall buildings. Moreover, the problem of data distribution shift between the training and testing phases exists for dynamically changing environments. These problems limit the robustness and generalizability of the data-driven GNSS positioning methods in urban areas. In this paper, a novel deep reinforcement learning (DRL) method is proposed to improve the robustness and generalizability of the data-driven GNSS positioning. Specifically, to address the data distribution shift in dynamically changing environments, the robust Bellman operator (RBO) is employed into the DRL optimization to model the deviations in the data distribution and to enhance generalizability. To improve robustness against task-irrelevant noisy GNSS measurements, the long-term reward sequence prediction (LRSP) is adopted to learn robust representations by extracting task-relevant information from GNSS observations. Therefore, we develop a DRL method with robust augmented reward sequence prediction to correct the rough position solved by model-based methods. Moreover, a novel real-world GNSS positioning dataset is built, containing different scenes in urban areas. Our experiments were conducted on the public dataset Google smartphone decimeter challenge 2022 (GSDC2022) and the built dataset Guangzhou GNSS version 2 (GZGNSS-V2), which demonstrated that the proposed method can outperform model-based and state-of-the-art data-driven methods in terms of generalizability across different environments.