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Tides not only affect regions along the coast, their periodic fluctuations are carried upstream inland through coastal rivers. River sections particularly affected by these tidal pulses are exposed to an increased risk of flooding. It is therefore important to localize these regions, as well as the extent of the river tide. However, until now, a global and accurate overview has not yet been established.

An international team led by the Centro Nacional de Investigación sobre la Evolución Humana (CENIEH) has just published a paper in the journal Quaternary Science Reviews reconstructing episodes of highstands of the Black Sea during the last glacial period, based on the analysis of coastal terraces in the eastern Sinop Peninsula (Turkey). These findings reveal alternating connections of the Black Sea with the Mediterranean and the Caspian Sea, refining the history of its highstands during the Late Pleistocene.

Sea ice around Antarctica expanded for several decades until a dramatic decline in 2015. The reasons behind this are revealed by research led by the University of Gothenburg, which is published in Nature Climate Change.

Beavers could engineer riverbeds into promising carbon dioxide sinks, according to a new international study led by researchers at the University of Birmingham. The paper, published in Communications Earth & Environment, has for the first time calculated the carbon dioxide (CO2) emitted and sequestered due to engineering work done by beavers in suitable wetland areas.

Author(s): Xiaofei Shen, Yue-Yue Chen, Karen Z. Hatsagortsyan, and Christoph H. Keitel

Relativistic electrons colliding with intense counterpropagating laser pulses are expected to lose energy through radiation reaction. However, we reveal a counterintuitive regime where reflected leptons (including incident electrons, generated electrons, and positrons) gain significant energies when…

[Phys. Rev. E 113, 035208] Published Wed Mar 18, 2026

SummaryPrevious studies of the 2021 Mw 7.4 Maduo earthquake have primarily focused on the early postseismic phase, while the dominant mechanisms driving postseismic processes and the seismic moment released by afterslip remain debated. Longer-term observational constraints are needed to address these issues. In this study, we integrate ~3.5 years of postseismic InSAR and GPS time series data to effectively separate the contributions of afterslip and viscoelastic relaxation. The results show that afterslip released a cumulative seismic moment of approximately 3.91 × 1019 N·m, accounting for ~23.3 per cent of the coseismic moment—equivalent to a new Mw 7.0 earthquake. The optimal steady-state and transient viscosities of the lower crust are estimated to be 1.35 × 1019 Pa·s and 1.5 × 1018 Pa·s, respectively. Afterslip remains the dominant mechanism driving near-field deformation throughout the observation period, while viscoelastic relaxation governed far-field deformation beginning about 4 months after the mainshock. The stress-driven afterslip is comparable with the inverted kinematic afterslip, and poroelastic rebound is negligible. These findings provide valuable insights into stress perturbations on surrounding faults induced by the coseismic rupture, afterslip, and viscoelastic relaxation, and offer new constraints on the recurrence interval of Mw 7.4 earthquake on the Jiangcuo Fault.

SummaryMachine learning models offer powerful predictive capabilities for geoscientific applications but remain limited by their ”black-box” nature and lack of rigorous uncertainty quantification. We developed a comprehensive, generalizable uncertainty quantification framework that decomposes predictive uncertainty into aleatoric and epistemic components using Quantile Regression Forests. Additionally, we applied unsupervised k-means clustering to isolate homogeneous data regimes, thereby reducing aleatoric uncertainty across spatially heterogeneous geoscientific datasets. To facilitate interpretation and quality assessment, we introduced five spatial diagnostic tools: bandwidth, variance, robustness, confidence, and explainability maps that characterize prediction reliability and identify dominant uncertainty sources. To demonstrate the framework’s applicability, we tested it on three synthetic datasets varying in size and a real-world geothermal heat flow application with 14 geophysical observables across continental Africa. Results show that clustering substantially reduces aleatoric uncertainty while maintaining stable epistemic uncertainty. Clustering also improves predictive accuracy and sharpens prediction intervals, with gains most pronounced in homogeneous regions. Applied to the African geothermal heat flow, the framework reveals region-specific geological controls (lithospheric architecture dominates stable cratons, while tectonic proximity governs active rift zones) and guides targeted data collection by distinguishing high-epistemic regions requiring additional sampling from high-aleatoric zones needing improved observables. While theoretically applicable to other geographic regions and geophysical datasets, the framework’s performance in different geological settings requires validation. This interpretable, uncertainty-aware approach enhances trustworthiness of predictions in spatially heterogeneous, data-sparse geoscientific problems.

How can we measure time more than 500 million years into the past? A study recently published in Nature Communications by researchers at the University of Lausanne presents a new geological "rock clock" that allows major climate events from the dawn of complex animal life to be dated with unprecedented precision.

New paired studies from the University of Minnesota Twin Cities show that machine learning can improve the prediction of floods. The studies, published in Water Resources Research and the Proceedings of the IEEE International Conference on Data Mining, demonstrate how "knowledge-guided" artificial intelligence can assist forecasters in saving lives and protecting infrastructure as the frequency of extreme weather increases.

Publication date: 15 March 2026

Source: Advances in Space Research, Volume 77, Issue 6

Author(s): Avinash Kumar Ranjan, Bikash Ranjan Parida, Jadunandan Dash, Amit Kumar Gorai

Publication date: 15 March 2026

Source: Advances in Space Research, Volume 77, Issue 6

Author(s): Zahraa Zawawi, Iman khudiesh, Ayah Helal

Publication date: 15 March 2026

Source: Advances in Space Research, Volume 77, Issue 6

Author(s): Peijuan Wang, Samet Aksoy, Elif Sertel

Publication date: 15 March 2026

Source: Advances in Space Research, Volume 77, Issue 6

Author(s): Yongwei Cao, Zhanghua Xu, Yuanyao Yang, Chaofei Zhang, Na Qin

Publication date: 15 March 2026

Source: Advances in Space Research, Volume 77, Issue 6

Author(s): Sirui Zhang, Bobin Cui, Shi Du, Guanwen Huang, Le Wang, Qin Zhang

Publication date: 15 March 2026

Source: Advances in Space Research, Volume 77, Issue 6

Author(s): Shuyue Wang, Jiawei Niu, Mohammed Bennamoun

Publication date: 15 March 2026

Source: Advances in Space Research, Volume 77, Issue 6

Author(s): Debasish Sing, Manas Das, Saraswati Das, Amit Kumar Mankar, Radhakanta Koner

A new study reveals an unprecedented increase in wildfires in tropical peatlands during the 20th century. "Unprecedented burning in tropical peatlands during the 20th century compared to the previous two millennia" is published in Global Change Biology.

Navigating monolithic icebergs, massive ocean waves and sub-zero snowstorms, CSIRO research vessel (RV) Investigator is a workhorse for Antarctic science. In just over 11 years and spread across seven voyages, the vessel has now spent the equivalent of one full year, or more than 10% of its time, at sea delivering crucial research in Antarctic waters.

Reliable and scalable water level prediction is crucial in hydrology for effective water resources management, especially when considering challenges owing to climate change, urbanization, improper land use, and high-water demand. It directly impacts the availability and distribution of freshwater in rivers and reservoirs. Therefore, accurate forecasting via early warning systems is a highly useful technique for flood mitigation, agricultural irrigation, ecosystem and environmental sustainability, and numerous other applications.

New research reveals that changes following the recent and dramatic decline in Antarctic sea ice could help a low-nutritional species prosper, with major ramifications for food webs and biogeochemical cycles. The findings are published in the journal Marine Ecology Progress Series.