Soil Organic Carbon (SOC) fluctuations in agricultural fields play a critical role in determining soil fertility and carbon sequestration. Efficient, non-destructive monitoring of SOC is essential. This study utilized Unmanned Aerial Vehicles (UAVs) and four Machine Learning (ML) algorithms-Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Xtreme Gradient Boosting (XGB)-to predict SOC levels.Our research, set at the Danforth Center Field Research Site (FRS), comprised 35 sorghum genotypes in a complete block design. Using a GeoProbe drilling machine, we collected soil cores for compositional analysis through the Haney test. Concurrently, drone-captured multispectral images were processed to create orthomosaic maps and extract features. The ML algorithms effectively predicted SOC levels, with MLR models showing the highest accuracy (RMSE = 7.12 ppm; R = 0.74). We observed variation in SOC among genotypes, suggesting that genotype-specific traits could influence SOC estimation accuracy. Field plots planted with genotypes like SC1345, BTx623, and SAP-133 showed strong predictability with errors below 5%. In contrast, SC283 and SAP-154 had prediction errors exceeding 30%.UAV-based remote sensing offers promising avenues for soil health assessment, contributing to precision agriculture and sustainable land management. Future studies could benefit from integrating hyperspectral sensors to fully harness this technology's potential.

This study demonstrates the advantages of scale- and variable-dependent localization (SDL and VDL) on three-dimensional ensemble variational data assimilation of the hourly-updated high-resolution regional forecast system, the Rapid Refresh Forecast System (RRFS). SDL and VDL apply different localization radii for each spatial scale and variable, respectively, by extended control vectors. Single-observation assimilation tests and cycling experiments with RRFS indicated that SDL can enlarge the localization radius without increasing the sampling error caused by the small ensemble size and decreased associated imbalance of the analysis field, which was effective at decreasing the bias of temperature and humidity forecasts. Moreover, simultaneous assimilation of conventional and radar reflectivity data with VDL, where a smaller localization radius was applied only for hydrometeors and vertical wind, improved precipitation forecasts without introducing noisy analysis increments. Statistical verification showed that these impacts contributed to forecast error reduction, especially for low-level temperature and heavy precipitation.

Global and coastal ocean surface water elevation prediction skill has advanced considerably with improved algorithms, more refined discretizations and high-performance parallel computing. Model skill is tied to mesh resolution, the accuracy of specified bathymetry/topography, dissipation parameterizations, air-sea drag formulations, and the fidelity of forcing functions. Wind forcing skill can be particularly prone to errors, especially at the land-ocean interface. The resulting biases and errors can be addressed holistically with a machine-learning (ML) approach. Herein, we weakly couple the Temporal Fusion Transformer to the National Oceanic and Atmospheric Administration’s (NOAA) Storm and Tide Operational Forecast System (STOFS 2D Global) to improve its forecasting skill throughout a 7-day horizon. We demonstrate the transformer’s ability to enrich the hydrodynamic model’s output at 228 observed water level stations operated by NOAA’s National Ocean Service. We conclude that the transformer is a rapid way to correct STOFS 2D Global forecasted water levels provided that sufficient covariates are supplied. For stations in wind-dominant areas, we demonstrate that including past and future wind-speed covariates make for a more skillful forecast. In general, while the transformer renders consistent corrections at both tidally and wind-dominant stations, it does so most aggressively at tidally-dominant stations. We show notable improvements in Alaska and the Atlantic and Pacific seaboards of the United States. We evaluate several transformers instantiated with different hyperparameters, covariates, and training data to provide guidance on how to enhance performance.

Rupture speed Vr and stress drop Δτ are two key parameters that can characterize earthquake source and the associated potential for ground shaking. Despite their importance, a controversy has emerged in recent years regarding whether there is a positive or negative correlation between Δτ and Vr. Here I attempt to reconcile the controversy by presenting a context-based solution and a physics-based solution. The first solution calls for attention to the specific context under which Vr and Δτ are discussed, as their meanings and estimated values can vary between different studies. It is noted that a negative correlation between Δτ and Vr can result, at least partly, from a tradeoff effect inherent to certain analysis method. For the second solution, it is shown that the specific correlation between Δτ and Vr can depend on the condition of fracture energy Gc. Constant Gc often favors a positive correlation, whereas introducing a variability of Gc can lead to a negative correlation. More efforts are needed to improve the methods for estimating Vr and Δτ, and to explore other mechanisms that may explain the correlation between the two parameters.

On 8 October 2023 UTC, significant tsunamis were observed around Japan without any major tsunamigenic earthquake, associated with a series of 14 successive minor earthquakes (mb = 4.5–5.4) near Sofugan in the Izu-Bonin islands. To examine the cause of this tsunami, we estimated the horizontal locations of the tsunami source and temporal history of the seafloor displacement, using the tsunami data recorded by the ocean-bottom pressure gauges > ~600 km away. Our results showed the main tsunami source was an uplift located at a caldera-like bathymetric feature near Sofugan, suggesting the involvement of caldera activity in the tsunami generation. The total seafloor uplift was larger than ~3 m, and the uplift amount of each event gradually increased over time, reflecting an accelerating occurrence of multiple sudden caldera uplifts within only a few hours.

The Natural Resources Conservation Service (NRCS, formerly the Soil Conservation Service, SCS) unit hydrograph (UH) is one of the most commonly used synthetic UH methods for hydrologic modeling and engineering design all over the world. However, previous studies have shown that the application of the NRCS UH method for some ungauged watersheds in the state of Indiana produced unrealistic flood predictions for both the peak discharge and the time to peak. The objective of this work is to customize the NRCS UH by analyzing the role of its two key parameters, namely, the peak rate factor (PRF) and the lag time, in creating the runoff hydrograph. Based on 120 rainfall-runoff events collected from 30 small watersheds in Indiana over the past two decades, the observed UHs are derived and the corresponding PRF and lag time are extracted. The observed UHs in Indiana show that the mean value of PRF is 371, which is lower than the standard PRF of 484, and the NRCS lag time equation tends to underestimate the “true” lag time. Moreover, a multiple linear regression method, especially the stepwise selection technique, is employed to relate the NRCS UH parameters to the most appropriate geomorphic attributes extracted from the study watersheds. Both the statewide and regional regression models show that the main channel slope is a major factor in determining the PRF and lag time. A customized Indiana unit hydrograph (INUH) is derived with updated parameters and the Gamma function. Validation results show that the INUH provides more reliable and accurate predictions in terms of the peak discharge and the time to peak than the original NRCS UH for the watersheds in Indiana.

Salt marshes are ecosystems with significant economic and environmental value. With accelerating rate in sea-level rise, it is not clear whether salt marshes will be able to retain their resilience. Field and numerical investigations have shown that storms play a significant role in marsh accretion and that they might be crucial to salt marsh survival to sea-level rise. Here we present the results from two studies (Pannozzo et al., 2021a,b; Pannozzo et al., 2022) that used numerical and field investigations to quantify the impact of storm surges on the sediment budget of salt marshes within different sea-level scenarios and to investigate how sediment transport pathways determine marsh response to storm sediment input. The Ribble Estuary, North-West England, was used as a test case. The hydrodynamic model Delft3D was used to simulate the estuary morpho-dynamics under selected storm surge and sea-level scenarios. In addition, sediment samples collected with a monthly frequency from different areas of the marsh were analysed with sediments collected from possible sources to integrate field observations with the numerical investigation of sediment transport pathways during stormy and non-stormy conditions. Results showed that, although sea-level rise threatens the estuary and marsh stability by promoting ebb dominance and triggering a net export of sediment, storm surges promote flood dominance and trigger a net import of sediment, increasing the resilience of the estuary and salt marsh to sea-level rise, with the highest surges having the potential to offset sea-level effects on sediment transport and sediment budget of the system. However, although storm sediment input resulted to be significant for the accretion of the marsh platform and particularly for the marsh interior, data showed that storms mainly remobilise sediments already present in the intertidal system and only to a minor extent transport new sediment from external sources.ReferencesPannozzo N. et al., 2021. Salt marsh resilience to sea-level rise and increased storm intensity. Geomorphology, 389 (4): 107825.Pannozzo N. et al., 2021. Dataset of results from numerical simulations of increased storm intensity in an estuarine salt marsh system. Data in Brief, 38 (6): 107336.Pannozzo N. et al., 2022. Sediment transport pathways determine the sensitivity of salt marshes to storm sediment input. In preparation.

The strength of CO2 fertilisation is a major uncertainty across terrestrial biosphere models (TBMs) and is suggested to be overestimated without a representation of nitrogen (N) limitation. Here, we compare TBM projections with and without coupled C and N cycling over alternative future scenarios (the Shared Socioeconomic Pathways) to examine how representing N cycling influences CO2 fertilisation as well as the effects of a comprehensive group of physical and socioeconomic global change drivers. Because elevated N deposition and N mineralisation (driven by elevated temperature) have stimulated terrestrial C sequestration over the historical period, a TBM without N cycling must exaggerate the strength of CO2 fertilisation to compensate for these unrepresented N processes and to reproduce the historical terrestrial C sink. As a result, it cannot reliably project the future terrestrial C sink, overestimating CO2 fertilisation as CO2 increases faster than N deposition and temperature in future scenarios.

A document by Kaiwen Wang. Click on the document to view its contents.

Urban Green Spaces (UGSs) are proving to be most important part of urban area of a city. These green spaces not only provide psychological comfort to humans but also affect heat impact on city to a vast level. In a developing country like India, where urban growth is happening in a very fast and haphazard manner a little consideration is given to Green Spaces in city. Such a study has been conducted over Lucknow metro-city of Uttar Pradesh state of India. In this study, the relation between Land Use\Land Cover (LULC) and Land surface temperature (LST) has been tried to find. Using Landsat-8 OLI data (Band 3, 4 & 5) and Maximum Likelihood Classification algorithm, 6 Land Use classes are obtained, which are “Built-up”, “Vegetation”, “Shrub”, “Water”, “Fallow Land” and “Other”. In addition to these 4 Land Cover Indices namely Normalized Difference Built-up Index (NDBI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Barren Index (NDBaI) are also generated for same areas. LST is obtained using TIRS data (Band 10 & 11) and Split Window Algorithm by Radiative Transfer Equation. Ancillary data is used for digitization of 5 assembly constituencies (ACs) of Lucknow parliamentary constituency (PC) of Lucknow district namely, Lucknow Cantonment, Lucknow North, Lucknow East, Lucknow West and Lucknow Central has been done. River and Canal passing through these areas, are also considered in Urban Green Spaces as per Urban Atlas code 14100. 250m radius buffer is generated around Built-up pixels for analysis of impact of UGSs on LST. Cantonment and East Lucknow area having highest amount of UGSs in terms of “Vegetation”, “Shrub” & “Water” pixels due to presence of Forest area in both ACs. It is found that LST is positively related with all indices except for NDVI with strong negative correlation and R2 of 0.47 and highest R2 of 0.53 with NDBI. Among all 5 ACs best correlation between all 4 LC and LST values is found in Lucknow East AC with R2 > 0.64 for NDVI, NDWI and NDBI. Lucknow East AC is having least LST but there is very little difference between LST values of Built-up pixels having minimum UGSs present in 250m radius buffer around built-ups and Built-up pixels having no UGSs around. AC Lucknow Central is having 1 °C difference in LST values of such different Built-ups.

Seismic urgent computing enables early assessment of an earthquake's impact by delivering rapid simulation-based ground-shaking forecasts. This information can be used by local authorities and disaster risk managers to inform decisions for rescue and mitigation activities in the affected areas. Uncertainty quantification for urgent computing applications stands as one of the most challenging tasks. Present-day practice accounts for the uncertainty stemming from Ground Motion Models (GMMs), but neglects the uncertainty originating from the source model, which, in the first minutes after an earthquake, is only known approximately. In principle, earthquake source uncertainty can be propagated to ground motion predictions with physics-based simulations of an ensemble of earthquake scenarios capturing source variability. However, full ensemble simulation is unfeasible under emergency conditions with strict time constraints. Here we present ProbShakemap, a Python toolbox that generates multi-scenario ensembles and delivers ensemble-based forecasts for urgent source uncertainty quantification. The toolbox implements GMMs to efficiently propagate source uncertainty from the ensemble of scenarios to ground motion predictions at a set of Points of Interest (POIs), while also accounting for model uncertainty (by accommodating multiple GMMs, if available) along with their intrinsic uncertainty. ProbShakemap incorporates functionalities from two open-source toolboxes routinely implemented in seismic hazard and risk analyses: the USGS ShakeMap software and the OpenQuake-engine. ShakeMap modules are implemented to automatically select the set and weights of GMMs available for the region struck by the earthquake, whereas the OpenQuake-engine libraries are used to compute ground shaking over a set of points by randomly sampling the available GMMs. ProbShakemap provides the user with a set of tools to explore, at each POI, the predictive distribution of ground-motion values encompassing source uncertainty, model uncertainty and the inherent GMMs variability. Our proposed method is quantitatively tested against the 30 October 2016 Mw 6.5 Norcia, and the 6 February 2023 Mw 7.8 Pazarcik earthquakes. We also illustrate the differences between ProbShakemap and ShakeMap output.

Modern seismic networks provide a huge amount of data received in real-time, being impossible the manual identification of relevant events useful to monitor the activity of the volcano. Thus, many volcano observatories are interested in tools to perform an online, automatic analysis of the seismic activity. Machine Learning area provides various of Volcano-Seismic Recognition (VSR) systems designed to classify seismic events in real-time. However, only a few approaches can also detect them in a continuous data streams. Most of those VSR systems are based on the 2-step supervised paradigm: 1. A training database (X-DB) of a given volcano ’X’ is prepared with hundreds of events manually detected and classified according to their physical origin. 2. Statistical models are built analysing this DB, and are later used to automatically identify events in new data recorded at the volcano X. This supervised procedure is the major drawback to achieve a fast deployment of a VSR system for another volcano Y, as the preparation of its own Y-DB takes considerable time, and requires qualified operators and previous recordings, which is difficult for volcanoes without recent activity or which haven’t been monitored. In order to overcome these limitations, the EU-funded project ’VULCAN.ears’ focused on real-time, Volcano-Independent VSR (VI.VSR) approaches. It proposes alternative solutions based on state-of-the-art technologies as universal DBs and models, waveform standardisation and parallel architectures. Recent results obtained by mixing DBs from Popocatépetl, Colima, Deception and Arenal active volcanoes will be presented. We apply VULCAN.ears technologies to evaluate VSR systems on joint DBs built with data of several volcanoes. We also use volcano-independent models to automatically classify events of another volcano, analysing how the recognition accuracy varies as the training DB becomes more complex. All tests are carried out by an easy to use, user-friendly graphical application (geoStudio). All these achievements produce new insights useful to redesign the next-generation, portable and robust VSR systems.

Future changes in Sahel precipitation are uncertain because of large differences among projections from various models. In order to explore this uncertainty, we use a storyline approach which seeks to identify alternative plausible evolutions of Sahel precipitation and their driving factors. By analysing projections from the CMIP6 climate models, we show that changes in North Atlantic and in Euro-Mediterranean temperatures explain up to 50% of the central Sahel precipitation change uncertainty. We then construct several storylines of Sahel precipitation change based on future plausible changes in North Atlantic and Euro-Mediterranean temperatures. In one storyline, an amplified warming of both the North Atlantic and the Euro-Mediterranean areas promotes a northward shift of the West African monsoon, increasing precipitation over the central Sahel, while, in another storyline, a moderate warming in both regions is associated with a small change in precipitation over the central Sahel and a decrease in precipitation over the western Sahel, at the end of the 21st century. These results indicate that reducing uncertainty in future changes in Sahel precipitation could be achieved by reducing uncertainty in the future warming of the North Atlantic and the Euro-Mediterranean areas.

Journals, funding agencies, and researchers are more frequently expecting manuscripts to include links to shared research data. Effective data sharing requires that data be findable, accessible, interoperable, and reusable (FAIR), and is thus predicated on establishing a common understanding on how to communicate: data exchange standards, common data formats, controlled vocabularies, and a communal data repository. When conducting research, we still communicate in shorthand that is effective for everyone on the team who understands our context, but is lost when data is shared in the absence of that context. “Water temperature” means only one thing to my research team, yet can mean dozens of things outside of that context. Data sharing is thus an exercise in sharing not just the data, which is typically readily available, but also the context of that data, which requires additional effort. This effort is one of the barriers to sharing data. We’ll describe an alternative model for accepting data to a repository: the immediate ingestion of data regardless of its metadata quality, then behavioural nudges and crowd-sourcing features that ensure this data meets appropriate standards prior to publication. We’ll show a work-in-progress prototype software tool that supports this alternative model, capable of accepting and standardizing a research data set to use CF conventions and ISO 8601 dates.

A document by Sujan Pal. Click on the document to view its contents.

Meandering channels are ubiquitous features in intertidal mudflats and play a key role in the eco-morphosedimentary evolution of such landscapes. However, the hydrodynamics and morphodynamic evolution of these channels are poorly known, and direct flow measurements are virtually nonexistent to date. Here, we present new hydroacoustic data collected synchronously at different sites along a mudflat meander located in the macrotidal Yangkou tidal flat (Jiangsu, China) over an 8-day period. The studied bend exhibits an overall dominance of flood flows, with velocity surges of about 0.8 m/s occurring immediately below the bankfull stage during both ebb and flood tides. Unlike salt-marsh channels, velocities attain nearly-constant, sustained values as long as tidal flows remain confined within the channel, and reduce significantly during overbank stages. In contrast, curvature-induced cross-sectional flows are more pronounced during overbank stages. Thus, a phase lag exists between streamwise and cross-stream velocity maxima, which limits the transfer of secondary flows and likely hinders the formation of curvature-induced helical flows along the entire meander length. Our results support earlier suggestions that the morphodynamics of intertidal mudflat meanders does not strongly depend on curvature-induced helical flows, and is most likely driven by high velocities and sustains seepage flows at late-ebb stages, as well as by other non-tidal processes such as waves and intense rainfall events. By unraveling complex flow structures and intertwined morphodynamic processes, our results provide the first step toward a better understanding of intertidal mudflat meanders, with relevant implications for their planform characteristics and dynamic evolution.

A document by Christian Claudel. Click on the document to view its contents.

A unique event in 2020 was the nearly complete lack of ice cover on the shallow Kara Sea and Laptev Sea continental shelves at the end of October – a necessary antecedent to hypothesized continental-shelf clathrate methane releases’ runaway-warming feedback (not well-addressed in AR5). Although the current state of coupled Atmosphere/Ocean General Circulation Models (AOGCMs) is such that they cannot reliably predict resulting weather-pattern changes in detail, a potential lower-bound for the overall impact of such releases can be explored on a decadal scale; impact is an important factor in risk-assessment. Previous work* with a prescribed-ocean, atmospheric circulation model suggested a 0.01 C global temperature increase per gigaton of additional atmospheric methane burden. The inclusion of a coupled dynamic ocean model in the present work resulted in a tripling of that estimate to about 0.03 C global warming per gigaton increase in atmospheric methane burden. Additionally, much larger Arctic responses were observed when specifying model changes chosen to approximate increased cloud brightness over the Arctic ocean. In this work, the AR5 RCP8.5 GHG scenario was chosen as the baseline in order to most faithfully represent current and anticipated trends over the relatively short time period investigated here (i.e., 2020 – 2040). After an extended model spin-up, the model global mean temperature was constant for two decades of integration using a constant model-year (2019), and closely reproduced the mean warming rate reported for this time period in the AR5 data when run in baseline RCP8.5 transient-mode over the subject time period. As is typical for the AOGCM used in this work (AR5 GISS ModelE2.0 07.50.01), the modeled Arctic sea-ice losses were less than is currently observed in the real-world; hence, the results presented here are regarded as no more than a lower bound to the magnitude of expected climatic responses to such a release. *[essoar.org/doi/10.1002/essoar.10503094.1 (2019)]