A series of processes involving sediment delivery from channels, its dispersal onto the floodplain, and storage at their channel margins, often generates natural levees. When levees breach, they release water and sediment onto the floodplain, leading to crevasse splays or avulsions. Despite their importance in constructing floodplains and influencing channel mobility, levee growth is poorly understood. Presently, no model fully explains dynamic channel-levee evolution. A common assumption is that levee and channel bed aggradation rates are coupled. If they coevolve, levees should progressively accumulate along any aggrading channel belt, yet observations indicate otherwise. Using a one-dimensional numerical model, we investigate levee growth decoupled from channel bed aggradation across two flood scenarios wherein the flooded level: 1) exceeds the levee crest height (i.e., front loading); 2) is lower than the levee crest causing partial inundation of distal levee deposits (i.e., back loading). Initially, rapid levee aggradation confines the channel and increases bankfull depth, which mitigates flooding. As confinement slows levee growth, channel bed aggrades until bankfull depth is sufficiently reduced to trigger overflows. This releasing process increases flood likelihood and enhances overbank accumulation, promoting front loading and re-confining the channel. Our findings suggest aggradational channels may experience confined-release phases, characterized by episodic levee growth and fluctuations in bankfull depth. Rapid in-channel aggradation increases flood frequency and variability with more confined-release cycles. These results imply that river avulsions and associated floods might preferentially occur when the channel bed aggrades faster than adjacent levees, whereby the channel becomes shallower and destabilized.

Accurate prediction of the solar wind speed and arrival of solar transient events are among the most important but still open problems in space weather research. This article implements a Convolutional Neural Network (CNN) coupled Long-Short Term Memory cell (LSTM) deep-learning model for the prediction of solar wind speed at Sun-Earth L1 on a daily and 6-hourly basis. The model is trained and validated using Solar-wind speed observations at Sun-Earth L1. The proposed CNN-LSTM models are developed and trained using data for the period from 01 August 1996 to 31 December 2019 and tested with the data from 01 January 2020 to 30 June 2024. The daily prediction model can forecast solar wind speed with a RMSE of 52.12 km/s and an overall correlation coefficient of 0.79, while the 6-hourly model can forecast solar wind speed with a RMSE of 33.72 km/s and an overall correlation coefficient of 0.93. This shows that without explicitly building in physics based relationships, the Deep Learning based models are able to perform reasonably well compared to existing approaches.

Pedotransfer functions (PTFs) are widely used to estimate soil hydraulic properties (SHPs) from easily measurable characteristics. However, most existing PTFs rely on unimodal hydraulic models, which fail to accurately represent the bimodal SHPs caused by soil structure common in field conditions. In this study, we developed new PTFs using two bimodal soil hydraulic models and introduced soil physics-informed neural networks (SPINN) to embed the models into PTF training. The results showed that the new PTFs effectively captured bimodality in hydraulic conductivity curves, achieving an RMSE of 0.578 in the test set, compared to 0.709 for unimodal models. The PTFs also improved soil water retention curve (SWRC) predictions but struggled with bimodal SWRCs for some samples, likely due to the limited number of bimodal SWRCs in the dataset. An independent dataset evaluation revealed that the RMSE for hydraulic conductivity predicted by the new PTFs was approximately one-third of that of classic PTFs. This underscores the significant role of soil structure in SHPs, which classic PTFs fail to capture. Additionally, PTFs developed using the SPINN method outperformed those optimized fitted hydraulic parameters via machine learning, a common approach in the literature. We also found that separate versus simultaneous optimization of water retention and hydraulic conductivity greatly affects PTF performance. Finally, we provided global 1 km-resolution maps of soil hydraulic parameters for the bimodal model.

This paper presents a novel hybrid deterministic-stochastic river morphodynamics numerical modeling approach that integrates a two-dimensional (2D) hydrodynamic model (deterministic) with a bed morphodynamic model (deterministic) and a bank erosion model (Markovian stochastic). The model solves the 2D Shallow Water Equations and the standard k-epsilon turbulence model. Bedload transport is estimated using the Meyer-Peter and Muller formula, and bed evolution is solved using the Exner Equation. The Markovian stochastic bank erosion model uses a new method to evaluate bank erosion risk. The approach was applied to a meander bend cutoff event in the Maiqu River on the Tibetan Plateau. Sixteen different bank-material critical shear stress cases were considered, representing highly erodible banks to resistant banks. Ten statistical realizations were performed for each case with different bank-material erodibility to obtain ensemble-averaged results. Flow field and bed evolution in the cutoff channel suggest that the model can successfully simulate bank erosion processes during the cutoff channel evolution, and bank topographic irregularities are reasonably captured. A newly introduced calibration parameter, the ratio of mesh size to the coupling period between the bank erosion model and the hydrodynamic & morphodynamic model, is not as intuitive as the erosion resistance calibration parameters used by the traditional bank erosion model. The proposed approach necessitates estimating the size of a typical slump failure, or 10-30% of the cutbank height, to set the size of the computational mesh.

Understanding equilibrium climate sensitivity (ECS, warming in response to a doubling of CO2) uncertainty is fundamental for making reliable climate projections. We leverage the Hector simple climate model in a probabilistic framework to explore how different ECS priors influence long-term (2081-2100) temperature anomalies. Specifically, we quantify how different ECS prior distributions broaden the uncertainty in temperature projections. This method demonstrates a computationally efficient probabilistic workflow that explores the effects of parameter priors on climate projections. Excluding process and paleoclimate evidence widens the resulting temperature projection uncertainty (1.12-3.03 ℃ and 1.09-3.33 ℃, respectively) while using priors that synthesize all lines of evidence leads to narrowed temperature projection uncertainty (1.24-2.89 ℃). Our study shows that excluding paleoclimate and process data in ECS priors broadens temperature projection uncertainty. In contrast, synthesized evidence provides a narrower and potentially more robust range of future temperature outcomes.

Electrical resistivity tomography (ERT) is a geophysical technique that is often used to characterize and monitor permafrost. Automated ERT (A-ERT) systems enable the collection of temporally dense datasets without the need for repeated site visits. So far, only a few A-ERT systems have been deployed in permafrost environments. We present the results of an A-ERT system installed on King George Island, Antarctica. The system withstood the harsh Antarctic environment and collected resistivity data four times a day for a full year in 2022-2023. Resistivity data captured seasonal freezing and thawing, as well as short-term meteorological events. Resistivity was used to estimate changes in unfrozen water content, and co-located temperature and soil moisture data were used to assess relationships between resistivity, soil moisture, and temperature. This work showcases the ability of A-ERT systems to monitor permafrost and soil moisture dynamics and contributes a unique dataset to global permafrost monitoring efforts.

Hamelin Pool, W.-Australia, is a natural laboratory to study stromatolite formation in a hypersaline lagoon. Stromatolitic aragonites sampled at supratidal, intertidal and subtidal environments show gradual increase in Ba isotopic compositions (d138Bacarb = -0.12-0.57‰) with decreasing Ba/Ca following conservative mixing between groundwater and seawater. We observe fingerprints of the groundwater end-member in a supratidal sample showing particular light d138Bacarb corresponding with elevated Mn/Sr and low d18Ocarb as indicator for discharge from the local Tamala Limestone aquifer. In contrast, stromatolitic carbonates formed in equilibrium with a saline end-member show heavy d138Bacarb. Lagoonal d138Bafluid ranges from 0.44-0.6‰ at increasing PSU (3.6-65.6). The calculated partition coefficient for Ba into stromatolites is lowest at the subtidal (logDBa=0.45) and highest at the groundwater discharge site (logDBa=0.86). Injection of groundwaters into Hamelin Pool likely contributes to enhanced aragonite precipitation at up to 0.32mm/yr, possibly catalysed by nucleation within or onto extra-polymeric substances of microbially diverse mats.

A document by Hirohiko Masunaga. Click on the document to view its contents.

We use atmospheric profiles from ERA5, JRA55 and MERRA2 between 1993 and 2023 to estimate Earth’s global clear-sky longwave feedback strength on the seasonal and interannual timescale. Differences in the relationship of relative humidity with skin temperature prior to 2008 lead to interannual feedback strengths between 1.34 W m−2 K−1 (JRA55) and 1.89 W m−2 K−1 (MERRA2). Restricting the analysis to the last 16 years yields more consistent interannual estimates of 2.05 W m−2 K−1 on average, which is larger than the overall seasonal estimate of 1.91 W m−2 K−1. The mid-tropospheric drying causing this difference suggest a substantial influence of ENSO variability on the interannual timescale. This indicates a long-term feedback strength smaller than 2.0 W m−2 K−1, which is already at the lower end of previous estimates; emphasizing the importance of accurate long-term RH measurements to reliably project Earth’s clear-sky feedback strength

Transported mineral dust in a Saharan air layer (SAL) contains highly active ice-nucleating particles (INPs) that may be transported across the Atlantic Ocean and subsequently seed clouds in the Caribbean and the Americas. During an aircraft campaign around Houston and the western U.S. Gulf Coast, a widespread SAL advected into the sampling region allowing for measurement of the ice-nucleating ability of SD following long-range transport. Results showed that the mean INP concentrations were 3–4.5 times higher than non-Saharan dust (nSD), but only at temperatures < –21 ºC. Active surface site densities were also enhanced in the SD, exceeding the mean for nSD by over an order of magnitude at temperatures < –21 ºC. These INP measurements confirmed that SD remains a highly active INP even after > 8000 km westward transport across the Atlantic Ocean.

A document by Masao Ishii. Click on the document to view its contents.

 Net and anthropogenic CO 2 exchange in the Pacific represents a major contribution to the global CO 2 sink.  The Pacific is thought to have turned to a net sink of CO 2 that is growing primarily due to the increasing uptake of anthropogenic CO 2.  The equatorial region is key in determining net and anthropogenic CO 2 uptakes and their trends and variability in the Pacific.

The commercial synthetic aperture radar (SAR) market is experiencing year-over-year growth and is currently capable of imaging anywhere in the world within an hour at X-band. Surface Deformation and Change (SDC) is a mission study at NASA to investigate innovative architectures beyond the NASA-ISRO SAR (NISAR) mission in the next decade. In this article we present preliminary findings on the technical capabilities of the currently available commercial SAR data, and investigate their applicability to SDC goals, such as imaging quality and retrieval of surface motion.

Moisture transport within atmospheric rivers is driven by a complex combination of processes, including convergence of moisture from different origins which change over the atmospheric river’s life cycle. The water vapor budget within an atmospheric river enables us to understand moisture sources and sinks (horizontal flux, evaporation and precipitation). Here, we focused on the water vapor budget of the exceptional atmospheric river associated with the storm Dennis that led to record-breaking precipitation on February 15th 2020. We used the WRF model to simulate the event and applied our new water vapor budget approach to the tracked atmospheric river. We also performed two sets of sensitivity experiments: one reducing the tropical moisture, and the other modifying the ocean evaporation to assess how these two main moisture sources affect the water vapor balance within the atmospheric river. We also study changes in the atmospheric river, cyclone and associated precipitation at landfall in the sensitivity experiments. For Dennis, tropical moisture played a prominent role in the early stages of the atmospheric river, while ocean evaporation became critical later. Additionally, the reduction of evaporation and also of tropical moisture is related to a decrease in precipitation over Europe. This study offers a new approach to understanding the evolution of atmospheric rivers and highlights the importance of different moisture processes. It provides a case study that helps to unravel feedback mechanisms and the impact of different perturbations on the water vapor balance of atmospheric rivers.

A document by Mason Perry. Click on the document to view its contents.

Clarifying the relationship between regular earthquakes and slow fault slip is essential for understanding the mechanisms behind seismic activity. We hypothesize that the background seismic activity around the Guerrero seismic gap in Mexico is partially triggered by interplate slow-slip events (SSEs). Consequently, we present an extension of the spatio-temporal epidemic-type aftershock sequence (ETAS) model, which incorporates background seismicity as a piecewise constant function over time. In this study, Global Navigation Satellite System (GNSS) data is employed to identify the occurrence periods of SSEs, thereby delineating the intervals during which changes in background seismicity may occur. Due to the technical complexity of performing inference with an inhomogeneous ETAS model, this work employs a penalized maximum likelihood inference method using the Expectation-Maximization (EM) algorithm. This approach also permits the inference of the branching process for aftershocks, thus enabling the estimation of the genealogy between earthquakes. This information could be utilized to decluster earthquakes. This study elucidates how the background seismicity increases during the periods of the Guerrero SSEs, which allows for a more comprehensive understanding of seismic activity and the relationship between slow and fast earthquakes in Mexico. Our new model can be applied not only in Mexico but also at plate boundaries worldwide to quantify the impact of SSEs on seismic activity.

During the Last Glacial Maximum, wetter and cooler conditions drove expanded pluvial lake systems throughout the southwestern United States. Here, we aim to understand the drivers of these hydroclimate changes using new biomarker records from Cumbres Bog, a high-elevation fen in the San Juan Mountains of southern Colorado. The leaf wax hydrogen isotope record, which we interpret as reflecting changes in the proportions of seasonal precipitation sources reaching Cumbres Bog, indicates that winter moisture delivery was reduced at the LGM, increased suddenly during the deglaciation at ca. 14.8 kyr, and then declined rapidly at ca. 11.5 kyr in association with exceptional early Holocene drought. Winter precipitation abruptly recovered at ca. 8.2 ka and remained high throughout the mid- to late-Holocene. BrGDGT-derived temperatures indicate that the southern Rockies were ca. 6.5°C colder during the LGM. Deglacial warming commenced at ca. 18.1 ka, reaching peak temperatures ~2°C greater than present in conjunction with the peak mid-Holocene drought. Deglacial warming was interrupted by small (~1-2°C) abrupt cooling events coincident with the YD and HS1. These deglacial and Holocene hydroclimate changes were accompanied by changes in terrestrial vegetation, lake evolution, and microbial community changes. Indices based on isoGDGT abundances reveal microbial community changes associated with primary succession and the expansion of methanogenic Archaea with deglacial warming. Past temperature variations significantly altered carbon cycling at the Cumbres Bog site and suggest that high alpine lacustrine ecosystems could be particularly vulnerable to future anthropogenic warming.

We explore the characteristics of EMIC waves generated in a non-dipole, compressed magnetic field at the minimum of the magnetic field. We conducted 2D full-wave simulations using the Petra-M code, focusing on a compressed dipole magnetic field line in the outer dayside magnetosphere for a range of L values ($L=8-10$). By comparing the simulation results with MMS observations, we aim to understand how the observed wave characteristics are affected by a shifting source region across different L-shells. Our findings indicate that the direction of the Poynting vector systematically changes depending on the local source location of the wave, which is consistent with the observations. EMIC waves propagate along the magnetic field line and reach both the northern and southern hemispheres; however, there is a notable difference in the power of EMIC waves between the two hemispheres, indicating seasonal asymmetries in their occurrence.