166 research outputs found
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Generating Boundary Conditions for Compound Flood Modeling in a Probabilistic Framework
Full text linkCompound flood risk assessments require probabilistic estimates of flood depths and extents that are derived from compound flood models. It is essential to simulate a wide range of flood driver conditions to capture the full range of variability in resultant flooding. Although recent advancements in computational resources and the development of faster compound flood models allow for more rapid simulations, generating a large enough set of storm events for boundary conditions remains a challenge. In this study, we introduce a statistical framework designed to generate many synthetic but physically plausible compound events, including storm-tide hydrographs and rainfall fields, which can serve as boundary conditions for dynamic compound flood models. We apply the proposed framework to Gloucester City in New Jersey, as a case study. The results demonstrate its effectiveness in producing synthetic events covering the unobserved regions of the parameter space. We use flood model simulations to assess the importance of explicitly accounting for variability in mean sea level (m.s.l.) and tides in generating the boundary conditions. Results highlight that m.s.l. anomalies and tidal conditions alone can lead to differences in flood depths exceeding 1 and 1.2 m, respectively, in parts of Gloucester City. While we use historically observed events, the framework can be applied to model output data including hindcasts or future projections.</p
Deformation Monitoring and Prediction of Open-pit Mine Slope Based on GBInSAR Technology and LSTM Neural Network
Full text linkHigh-precision and real-time deformation monitoring of open-pit mine slopes provides reliable data support for slope safety early warning and governance, which is directly related to the production efficiency of open-pit mines and the safety of personnel and property. Compared with traditional measurement technologies, Ground-Based Synthetic Aperture Radar Interferometry (GBInSAR), as a novel deformation monitoring technology developed in the past two decades, possesses significant advantages including allweather operation, all-time coverage, large-scale monitoring, non-contact measurement, high precision, and real-time observation. It has become one of the core technical equipment for monitoring dangerous slopes in open-pit mines and is widely applied in open-pit mine slope safety monitoring scenarios. Against this background, how to effectively integrate ground-based InSAR data with advanced prediction models to enhance the early prediction capability of slope deformation has become a crucial research direction in this field. To address this issue, this paper proposes a GBInSAR time-series data processing method based on the Long Short-Term Memory (LSTM) model. Firstly, the initial deformation information of a slope is extracted from the pre-monitoring data of the IBIS system, and then an LSTM-based slope deformation prediction model is constructed to achieve short-term accurate prediction of future deformation trends. By organically combining the LSTM model with ground-based InSAR data, this paper deeply explores the temporal evolution characteristics of slope deformation and establishes a slope deformation prediction model. This study aims to explore the application of ground-based InSAR in slope deformation monitoring based on the LSTM model; by constructing a slope deformation prediction model and a risk early warning mechanism, it provides effective technical support and decision-making basis for the safety management of open-pit mine slopes
Spatiotemporal mapping of invasive yellow sweetclover blooms using Sentinel-2 and high-resolution drone imagery
Full text linkYellow sweetclover (Melilotus officinalis (L.) Lam.; MEOF) is an invasive forb pervasive across the Northern Great Plains in the United States, often linked to traits such as wide adaptability, strong stress tolerance, and high productivity. Despite MEOF's prevalent ecological-economic impacts and importance, knowledge of its spatial distribution and temporal evolution is extremely limited. Here, we aim to develop a spatial database of annual MEOF abundance (2016–2023) across western South Dakota (SD) at 10 m spatial resolution by applying a generalized prediction model on Sentinel-2 imagery. We collected in situ quadrat-based total vegetation cover with MEOF percent cover estimates across western SD from 2021 through 2023 and synthesized with other available percent cover estimates (2016–2022) of several federal, state, and non-governmental sources. We conducted drone overflights at 14 sites across Butte County, SD in 2023 to develop very high spatial resolution (4–6 cm) and accurate MEOF cover maps by applying a random forest (RF) classification model. The field-measured and uncrewed aerial system (UAS) derived MEOF percent cover estimates were used to train, test, and validate a RF regression model. The predicted MEOF percent cover dataset was validated with UAS-derived percent cover in 2023 across four sites (out of 14 sites). We found that the variation in the Normalized Difference Moisture Index and Distance to roads were among the top predicting variables in predicting MEOF abundance. Our predictive model yielded greater accuracies with an R2 of 0.76, RMSE of 15.11 %, MAE of 10.95 %, and MAPE of 1.06 %. We further validated our 2023 predicted maps using the 3 m resolution PlanetScope imagery for regions where field samples could not be collected in 2023. The database of MEOF abundance showed consecutive years of average or above-average precipitation yielded a higher MEOF abundance across the study region. The database could assist local land managers and government officials pinpoint locations requiring timely land management to control the rapid spread of MEOF in the Northern Great Plains. The developed invasive MEOF percent cover datasets are freely available at the figshare repository (https://doi.org/10.6084/m9.figshare.29270759.v1, Saraf et al., 2025).</p
Cold winters, warm summers, no dry season: greenhouse gas emissions from forest organic soils in the Köppen–Geiger Dfb climate zone
No full textGreenhouse gas (GHG) emissions from organic soils are a key component of land-use-related emissions, particularly in countries with large areas of organic soils. Temperate-zone forest soils remain less studied in GHG research than boreal soils. However, recent work has expanded coverage in the northeastern temperate region, which, under the Köppen–Geiger climate classification, shares key climatic characteristics with the southern boreal region (Dfb). This study synthesised updated GHG flux data to evaluate carbon balance and emissions from forest organic soils in the Dfb zone, stratified by drainage status, nutrient availability, and dominant tree species. Such stratification revealed CO2 source-sink patterns, which encourage the ecological relevance of using these categories for data aggregation. The dominant tree species reflected nutrient status: drained coniferous and deciduous stands have been reported as CO2 sources, emitting 0.03 ± 0.55 and 0.47 ± 0.29 t CO2‑C ha−1 year−1, respectively, though soils tended to shift toward CO2 sinks in stands older than 25 years. In contrast, undrained soils have generally been observed to function as CO2 sinks, although not necessarily in all sites. However, this stratification was less informative for CH4 and N2O. CH4 fluxes were primarily determined by water table level rather than by other site variables, whereas N2O showed a tendency toward elevated emissions in deciduous stands, irrespective of drainage status
Residence time dynamics in fragmented river networks, a mechanistic modelling approach using optimal channel networks
Full text linkHydrological models often lack the capacity to explicitly connect river network topology with dynamic water balance processes and localised flow disturbances. Here, we present a novel modelling framework that integrates Optimal Channel Network (OCN) theory with time evolving precipitation–runoff dynamics and physically grounded representations of in-channel barriers (e.g. dams and weirs). Unlike traditional OCN implementations that remain hydrologically static, our approach simulates discharge, storage, and residence time dynamically across synthetic yet realistic river geometries. This coupling enables controlled numerical experiments to isolate the effects of network structure, hydroclimatic forcing, and flow fragmentation, effects that are otherwise difficult to disentangle in real-world systems. As a proof of concept, we investigate how flow disturbance structures alter channel network residence times under both steady and periodic flow regimes. We show that while outlet discharge remains virtually unchanged, local residence time at dammed nodes can increase by over 25 %, revealing strong spatial decoupling between upstream disturbance and downstream flow signals. By exploiting the self-affine scaling of OCN geometry, we further derive an analytical scaling law that links residence-time amplification around local flow disturbances to commonly available river-network metrics (slope–length and discharge–length exponents). This provides a transferable theory for residence-time impacts in fragmented networks that can be evaluated directly from network geometry, without requiring full numerical simulations. These findings have broad implications for modelling contaminant decay, microbial transport, and ecological connectivity, all of which depend critically on local hydrologic conditions. Our framework offers a generalisable, disturbance-aware platform for advancing the mechanistic understanding of river network behaviour under changing climate and anthropogenic impacts
The coupled Southern Ocean–Sea ice–Ice shelf Model (SOSIM v1.0): configuration and evaluation
No full textComplex interactions among the ocean, sea ice, and ice shelves in the Southern Ocean are critical for global climate, yet accurately simulating these processes remains challenging in climate models, such as those participating in the Coupled Model Intercomparison Project Phase 6, due to their coarse resolution and incomplete physical components. Therefore, the development of high-resolution circumpolar coupled ocean–sea ice–ice shelf models could improve our understanding of the evolution of the Southern Ocean. In this study, we use the c66m version of the Massachusetts Institute of Technology General Circulation Model, including a sea ice component and an ice shelf component, to configure the coupled Southern Ocean–Sea ice–Ice shelf Model (SOSIM v1.0). Adopting the Refined Topography dataset version 2 for the geometry of seafloor and ice draft, SOSIM features a horizontal resolution of ~5 km and 70 vertical layers. Forced by the European Centre for Medium-Range Weather Forecasts Reanalysis v5, a long-term integration of SOSIM is run forward from 1979 to 2022, with daily outputs for estimating the oceanic state, sea ice evolution, and basal mass balance of ice shelves. A comprehensive evaluation of the performance of SOSIM has been conducted against multiple observational and reanalysis datasets. Identified biases include an underestimated Antarctic Circumpolar Current transport, an overestimated Antarctic Slope Current, a warm drift in abyssal waters, an exaggerated seasonality of sea ice extent, and an underestimated total ice shelf mass loss. Despite these limitations, SOSIM still captures large-scale hydrographic structures, the annual variability of sea ice, and cross-slope exchanges over shelf seas. Furthermore, SOSIM is set to serve as the dynamical core for the next-generation Southern Ocean Ice Prediction System being developed in China
A new production-based model for estimating emissions and banks of ODSs: application to HCFC-141b
Full text linkThe Montreal Protocol on Substances that Deplete the Ozone Layer requires that the production of long-lived ozone-depleting substances (ODSs) that are intended for use in emissive applications be phased out. The Protocol does not, however, limit the release to the atmosphere of ODSs already existing in such applications and equipment. Accounting for emissions from these “banked” ODSs (e.g., in insulating foams) is important for monitoring the success of and compliance with the Protocol, for understanding where further mitigation of ODS emissions might be effective, and for estimating future ozone depletion. Here, we present a new bottom-up model that incorporates existing use and life-cycle information to calculate emissions and banks as well as uncertainties in the quantities. To demonstrate the model, we apply it to 1,1-dichloro-1-fluoroethane (HCFC-141b), a chemical used primarily in foam insulation and whose production is currently being phased out. We calculate global emission trends that are qualitatively similar to those derived from atmospheric measurements from 1990 to 2017. After 2017, our calculated emissions no longer track the observationally based trends through the end of the comparison in 2021. This discrepancy suggests either a growing additional source of emissions that is inconsistent with reported production or a model deficiency that was not apparent before 2017. Our calculations also show that the easily recoverable portion of the bank will be smaller in the future than the total bank estimated in other recent work, with implications for the feasibility of recovering banks before the release of HCFC-141b to the atmosphere.</p
Experimental protocol for phase 1 of the APARC QUOCA (QUasibiennial oscillation and Ozone Chemistry interactions in the Atmosphere) working group
Full text linkThe quasi-biennial oscillation (QBO) is the main mode of variability in the tropical stratosphere, influencing the predictability of other regions in the atmosphere through its teleconnections to the stratospheric polar vortices and coupling to surface tropical and extratropical variability. However, climate and forecasting models consistently underestimate QBO amplitudes in the lower stratosphere, likely contributing to their failure to simulate these teleconnections. One underexplored contributor to model biases is missing representation of ozone-radiative feedbacks, which enhance temperature variability in the lower stratosphere, particularly at periods at and greater than the QBO (>28 months). While previous studies suggest that ozone-radiative feedbacks can impact QBO periods, amplitudes and the associated secondary circulation in the lower stratosphere, the reported impacts differ widely among models and are hard to interpret due to differences in methodology. To this end, here we propose a coordinated experimental protocol – held joint between the Atmospheric Processes and their Role in Climate (APARC) Quasi-Biennial Oscillation Initiative (QBOi) and Chemistry Climate Modeling Initiative (CCMI) activities – which is aimed at assessing the coupling between stratospheric ozone, temperature and the circulation. We use the proposed experiments to define the ozone feedback on the QBO in both present-day and idealized (abrupt quadrupling of carbon dioxide) climates. While primary focus is on the QBO, the proposed protocol also enables analysis of other aspects of ozone-radiative-dynamical coupling in the atmosphere, including impacts on the Brewer-Dobson Circulation and tropospheric eddy-driven jet responses to future climate change. Here we document the scientific rationale and design of the QUOCA Phase 1 experiments, summarize the data request, and give a brief overview of participating models. Preliminary results using the NASA Goddard Institute for Space Studies E2-2 climate model are used to illustrate sensitivities to certain methodological choices.</p
Linking ridge shapes to the ice thickness distribution via discrete element simulations
Full text linkRidges significantly increase the sea-ice thickness compared to the level ice surrounding them. In continuum sea-ice models, this increase is either represented by an increase in mean ice thickness or by changes in the ice thickness distribution (ITD). The implementation of ITDs requires a sub-grid parametrization of ridging by using a redistribution scheme. In contrast, the discrete element method (DEM) enables explicit simulations of ridge formation process, including ice fragmentation into rubble and its subsequent redistribution to ridges. Here, we use a DEM model to simulate ridging across a sea ice domain of size 6 km x 6 km. The DEM simulations yield deformed ice cover with ridges of varying shapes, namely triangular and trapezoidal ridges; the trapezoidal ridges notably affect the ITD of the deformed ice cover by creating a bump in the ITD towards thicker ice. We find that the ITD of the deformed ice field from DEM simulations differs from those from the continuum model, that uses only mean thickness, and from two commonly used ridging functions within redistribution schemes used as sub-grid parametrizations. Further, we show how to formulate an analytical redistribution function that captures the effect of various ridge shapes and discuss when it could replace existing ridging schemes. Our results demonstrate that an improved representation of ridging is needed within continuum models to resolve ridges both with their depth and shape within the ITD, especially in high spatial resolutions. Additionally, we formulate open questions in need of answers to allow implementation of our new distribution of ridged ice into continuum models, which connect to the ridging process itself
Estimating Ground-Level Carbon Monoxide Concentrations Using Machine Learning Techniques: The Metropolitan City of Milan Case Study
Full text linkThis work presents a structured data-driven framework for estimating ground-level carbon monoxide (CO) concentrations in the Metropolitan City of Milan (MCM) by integrating Sentinel-5P satellite observations, Copernicus Atmosphere Monitoring Service reanalysis data, and ERA5 meteorological variables with advanced machine learning techniques. The methodology employs unified data preprocessing, systematic feature engineering (e.g., boundary layer height-adjusted CO, lagged meteorological variables), Bayesian optimization for hyperparameter tuning, SHAP-based feature selection, model ensembling, and robust statistical validation. Eight regression models, including a custom Dense Attention Network (DAN), were evaluated across multiple temporal aggregation windows (4–24 hours before 15:00 GMT+1) to identify optimal configurations for CO estimation. Using data from January 2019 to November 2024, the framework identified the 21:00–15:00 GMT+1 window as most effective for capturing atmospheric dynamics such as nighttime accumulation, morning emission peaks, and daytime dilution. The DAN achieved the best performance, with a mean normalized root mean squared error of 0.4879 ± 0.0252 on the test set, outperforming ensemble and traditional regression models, offering a scalable, interpretable, and cost-effective approach to urban CO monitoring in data-scarce environments with potential adaptation to other pollutants and regions