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We assessed 6122 environmental modelling papers published since 2005 to determine whether the number of citations each paper had received by September 2014 could be predicted with no knowledge of the paper's quality. A random forest was applied, using a range of easily quantified or classified variables as predictors. The 511 papers published in two key journals in 2008 were further analysed to consider additional variables. Papers with no differential equations received more citations. The topic of the paper, number of authors and publication venue were also significant. Ten other factors, some of which have been found significant in other studies, were also considered, but most added little to the predictive power of the models. Collectively, all factors predicted 16–29% of the variation in citation counts, with the remaining variance (the majority) presumably attributable to important subjective factors such as paper quality, clarity and timeliness. [ABSTRACT FROM AUTHOR]

Underwater fish segmentation technology serves as a crucial foundation for extracting aquatic biological information. However, due to intricate and fluctuating underwater environments, existing segmentation models fail to precisely focus on key image regions. Based on this, the paper developed an underwater fish segmentation model, Receptive Field Expansion Model(RFEM), by enhancing soft attention performance (More attention is directed to fish regions when processing fish pixels). This paper tests ten different attention mechanisms and selects the attention mechanism with better performance indicators to improve it and form an RFEM model. This paper uses two underwater fish data sets to verify the proposed model. The experimental results show the segmentation mean intersection-over-union ratio (MIoU) of RFEM based on dilation convolution reached 88.37%, and the mCPA reached 93.83%, Accuracy reached 96.08%, and F1-score reached 93.74%. It can provide solid technical support for intelligent monitoring such as body length measurement, weight estimation of underwater fish. • A fish image segmentation model in complex scenes is proposed for aquaculture. • A method based on dilation convolution has better performance of soft attention. • An image enhancement algorithm can address limitations of restricted datasets. [ABSTRACT FROM AUTHOR]

With the development of geographic simulation methods in recent decades, a great deal of resources have accumulated to support their implementation. These resources can be divided into model resources for analyzing or predicting geographic phenomena or processes, data resources for representing the characteristics of real or simulated environment, and computing resources for supporting simulation tasks. These resources are characterized by geospatial distribution and are difficult to discover and reuse. OpenGMS has carried out a series of fundamental research to sharing and collaborating distributed resources on the web. On this basis, this paper presents the concept of the OpenGMS open portal. The portal adopts FAIR principles and supports resources sharing and reuse to facilitate collaboration and exchange between resource contributors and users. This paper takes applications of the portal in resource sharing and reuse case and online training courses as examples to illustrate how the portal can bridge contributors and users of resources. • The portal adopts FAIR principles and is integrated into OpenGMS platform. • The functional design for resource contributors and users is presented. • The portal attracts many users and amasses lots of geographic simulation resources. • The portal assists in model sharing and reuse among researchers in cyberspace. [ABSTRACT FROM AUTHOR]

Hydrodynamic models with rain-on-the-grid capabilities are usually computationally expensive for automatic parameter estimation. In this paper, we present a global optimization-based algorithm to calibrate a fully distributed hydrologic-hydrodynamic and water quality model (HydroPol2D) using observed data (i.e., discharge, or pollutant concentration) as input. The algorithm finds near-optimal set of parameters to explain observed gauged data. This framework, although applied in a poorly-gauged urban catchment, is adapted for catchments with more detailed observations. The results of the automatic calibration indicate NSE = 0.99 for the V-Tilted catchment, RMSE = 830 mg L-1 for salt concentration pollutograph in a wooden-plane (i.e., 8.3% of the event mean concentration), and NSE = 0.89 in a urban real-world catchment. This paper also explores the issue of equifinality (i.e., multiple parameters giving the same calibration performance) in model calibration indicating the performance variation of calibrating only with an outlet gauge or with multiple gauges within the catchment. [Display omitted] • An automatic calibration algorithm for distributed flood and water quality modeling is developed. • It uses HydroPol2D model and calibrate water quantity and quality parameters globally. • Data from observed gauges such as discharges, depths, and concentration is used for calibration. • Poorly placed gauges and low runoff events can increase equifinality during calibration. [ABSTRACT FROM AUTHOR]

This paper presents improved explicit local time-stepping (LTS) schemes of both first and second order accuracy for storm surge modeling. The two-dimensional shallow water equations are numerically solved on unstructured triangular meshes using finite volume method with Roe's approximate Riemann solver. The LTS algorithms are designed based on explicit Euler and strong stability preserving Runge–Kutta time integration methods. A single-layer interface prediction–correction scheme is adopted to combine coarse and fine time discretization, further enhancing the stability of the LTS schemes, particularly at higher LTS levels and during long time simulations. An ideal numerical test validates the efficiency of the improved LTS models, revealing their capability to improve computational speed while preserving conservation properties and reducing accuracy loss as LTS levels increase. We further apply the LTS models to cross-scale simulations of storm surges in the Northwest Pacific. Results show that compared to the global time-stepping (GTS) models, the LTS models significantly boost computational speed by up to 37%, all while delivering equally reliable computational outcomes. With expanding high-resolution coastal data and the need for high-resolution modeling, the improved LTS models show great potential for cross-scale storm surge modeling. • This paper develops improved explicit first and second-order local time-stepping (LTS) schemes for two-dimensional shallow water equations. • The improved LTS schemes enhance computational efficiency while ensuring stability at higher LTS levels and during extended simulations, surpassing original LTS methods. • The LTS models achieve up to 37% efficiency gain in Northwest Pacific surge simulations, revealing their broad application potential. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive Virtual Reality (VR) based framework for visualizing numerical simulations of geophysical flows in a realistic and immersive manner. The framework allows integrating output data from various mesh-based Eulerian numerical models into a VR environment, enabling users to interact with and explore the terrain and geophysical flows through the VR experience. Three case studies, including a snow avalanche, quick clay landslide, and flash flood in Norway, demonstrate its versatility. The VR environment offers intuitive menus and user interactions, allowing users to read flow depth and velocity values, facilitating a direct link between numerical data and their visual representation. This framework can reshape geophysical flow hazard identification and disaster management by integrating physics-based numerical modeling results into VR Environments, thus enhancing knowledge dissemination among experts, the general public, non-expert stakeholders, and policymakers. The paper also highlights challenges and opportunities identified during the integration, guiding future developments. • A framework that integrates numerical simulations of geophysical flows into VR. • The framework is versatile across different geophysical flow types and numerical models. • Real time visualization of numerical simulation results is still a challenge. • The framework has a potential for disaster preparedness and emergency training. [ABSTRACT FROM AUTHOR]

In view of upcoming more stringent air quality limits and the ambition to align with the WHO guidelines, nitrogen dioxide (NO 2) pollution from traffic and other sources will remain a problem in the EU. To assess the impact of traffic measures and emission reductions in other sectors on NO 2 -concentrations, an EU-wide high-resolution NO 2 source apportionment web-application was developed. The application allows users to define scenarios in a user-friendly way and quickly visualize the NO 2 -concentrations at measurement stations and in cities. The user can configure a new Euro 7/VII emission standard and additionally define urban access regulations scenarios in cities. To capture the spatial scales of NO 2 pollution, the SHERPA source-receptor model was used in combination with the QUARK kernel dispersion model. The first model considers long-distance impacts, the latter considers the strong concentration gradients close to roads. This paper focuses on the methodology, a follow-up paper describes the web-application. • We present a method for fast sectoral and spatial NO 2 source apportionment. • The road transport sector is considered in high detail and at 100-m resolution. • The impact of a new vehicle emission standard for NO x can be simulated. • The effect of urban access regulations can be simulated in 948 European cities. • NO 2 concentration effects are visualized at measurement stations and over cities. [ABSTRACT FROM AUTHOR]

This paper describes the DAnish Lagrangian Model (DALM), which is a new high-resolution air pollution model based on the concept of Lagrangian particles. The new model is developed from the simpler Gaussian plume-in-grid Urban Background Model (UBM) and is to be integrated with the DEHM/UBM/AirGIS modeling system, developed at Aarhus University. In the first part of the paper, the theoretical foundation of the model is presented, and the implementation of a large set of physical and numerical parameterizations is discussed. The second part describes the validation of DALM against measurements, applying different combinations of the implemented parameterizations. This validation demonstrates that DALM can accurately reproduce spatiotemporal patterns in the measured data and that its performance is more sensitive to parameterizations of vertical compared to horizontal transport. Conclusively, the combination of parameterizations yielding the best model performance is determined based on a ranking system, and future improvements to DALM are outlined. [Display omitted] • A new comprehensive local-scale Lagrangian air pollution model is in development. • The new model is validated against measurements from Danish monitoring stations. • Some sets of planetary boundary layer parameterizations show a better performance. [ABSTRACT FROM AUTHOR]

Geographic modeling is considered as an effective way to solve geographic problems. Geographic conceptual modeling is the first step of geographic modeling, in which modelers can fully exchange modeling ideas, thereby achieving a common understanding of geographic system and guiding subsequent geographic modeling process. However, due to the diverse research backgrounds of modelers, it is challenging for them to exchange modeling ideas with one another. This paper provides a visual cognitive communication method for modelers by constructing a conceptual graph gallery, and then improves the efficiency of geographic conceptual modeling. A multidimensional description method that describes modeling cognition from multiple perspectives, the conceptual graph gallery that includes concept items and conceptual graphs, and the conceptual graph gallery-based cognitive communication methods are designed in this paper. Finally, a case study involving the construction of a hydrological conceptual graph gallery is designed to illustrate the feasibility of conceptual graph gallery-based cognitive communication. • Cognitive communication is a key component in geographic conceptual modeling. • Geographical concepts can be described from different perspectives. • Concept graphs can describe geographic concepts visually. • Conceptual graph gallery can support cognitive communication. [ABSTRACT FROM AUTHOR]

Abstract: Treatment of industrial wastewaters is currently confronting important challenges concerning both cost management of treatment plants and fulfillment of tightening environmental regulations. Online monitoring of wastewater treatment is critical, because changes in the performance of treatment can lead to various problems such as decreased efficiency of purification, decreased energy efficiency, or ineffective use of chemicals. Moreover, changes in the operation of a treatment process can inflict changes that have unforeseen consequences, including an increased amount of harmful effluents, and therefore it is essential for a monitoring system to be able to adapt to various process conditions. It seems, however, that the monitoring systems used currently by the industry are lacking this functionality and are therefore only partially able to meet the needs of modern industry. In addition, there is typically a large amount of measurement data available in the industry, for which advanced data processing and computational tools are needed for monitoring, analysis, and control. For this reason, it would be useful to have a monitoring system which could be able to handle a large amount of measurement data and present the essential information on the state and evolution of the process in a simple, user-friendly and flexible manner. In this paper, we introduce an adaptive multivariable approach based on self-organizing maps (SOM) which can be utilized for advanced monitoring of industrial processes. The system developed can provide a new kind of tool for illustrating the condition and evolution of an industrial wastewater treatment process. The operation of the system is demonstrated using process measurements from an activated sludge treatment plant, which is a part of a pulp and paper plant. [Copyright &y& Elsevier]

Reliable probabilistic hydrological prediction requires appropriate handling of residual errors, which can pose considerable complexity. This paper proposes a nonparametric residual error (NRE) model that effectively captures the statistical characteristics of raw residuals. The NRE model employs a local linear estimator with a robust bandwidth selector to estimate the regression and conditional volatility functions of raw residuals. Additionally, the AR(1) model and location-mixture Gaussian distribution are used to estimate the temporal correlation structure and innovation distribution. Through two case studies in South-East China, this research demonstrates the superiority of the NRE model over the benchmark Box-Cox transformation approach in terms of prediction reliability, precision, and bias correction capabilities. Simulation experiments further reveal that the NRE model can effectively fit the residual regression function, conditional volatility function, and innovation distribution under varying scenarios. The proposed residual error model is anticipated to promote the adoption of probabilistic predictions in hydrological modeling applications. • Residual Error Handling: This paper tackles complex residual errors in hydrological modeling. • Accuracy Improvement: The NRE model boosts accuracy with a local linear estimator. • Predictive Superiority: The NRE model excels in predicting reliability, precision, and bias correction. • Data-Driven Flexibility: The NRE model reduces reliance on assumptions for hydrological modelers. [ABSTRACT FROM AUTHOR]

Groundwater level (GWL) prediction is important for ecological protection and resource utilization; it helps in formulating policies for artificial groundwater recharge, modifying the number of extraction wells, etc., and can support sustainable human development as well as inform water resource management decisions. However, climate change, anthropogenic impacts, and the complex coupling between surface water and groundwater increase the difficulty of predicting groundwater levels. The model proposed in this paper combines external data as well as multiple models. The method leverages long and short-term memory (LSTM) and convolutional neural network (CNN) models, combined with secondary modal decomposition and slime mould algorithm (SMA), together with an adaptive weight module (AWM). The study applies this method to predict GWL for three different hydrological conditions in China, specifically for the Jinan Baotu Spring, Heihu Spring, and Zhongtianshe watershed of Taihu Lake. A comparison of metrics such as mean absolute error and Nash efficiency coefficient for single and hybrid models shows that the model in this paper is more advantageous than the single model and other hybrid models. The interpretability of the model is enhanced by SHAP values that demonstrate the degree of contribution of the input variables. This paper uses SHAP analyses to identify the key drivers affecting groundwater levels. These factors must be detected in order to develop groundwater resource protection measures. [Display omitted] • Multivariate fusion data including hydrology and meteorology are used as model input. • A secondary modal decomposition module for historical groundwater level data was utilized. • The neural network hyperparameters are optimized using the slime mould algorithm. • Aggregate subsets of prediction with adaptive modules rather than linear summation. • The interpretable SHAP model measures the degree of influence of external variables. [ABSTRACT FROM AUTHOR]

Abstract: The Environmental Effects Monitoring (EEM) Statistical Assessment Tool (SAT) Decision Support System (DSS) has been developed to provide a user-friendly data analysis, display and decision support tool for Canada''s federal environmental effects monitoring program for the pulp and paper and mining industries. The target users include industries, consultants, regional EEM coordinators, National EEM Office and scientists involved in EEM-related research. The tool allows the assessment of the effects of effluent from industrial or other sources on fish and benthic populations. Effect endpoints, which are used as indicators of potentially important effluent effects, are measured at effluent-exposed sites and are compared statistically to measures at reference sites, in order to determine if changes have occurred and the magnitude of the changes. The main driver of the EEM-SAT DSS is its rule-based expert system. The results are used in assessing the adequacy of existing regulations for protecting aquatic environments. [Copyright &y& Elsevier]

The ability of Bayesian networks (BNs) to model complex systems and uncertainties makes it a perfect tool for the research on subfields related to climate change. In fact, in the past 30 years, BNs have been widely used in this field, with 1502 articles in total. Quantitatively understanding influential researchers, institutions, mainstream topics and research trends will help us quickly go deeper into this field. Thus, a scientometric method was conducted. In this paper, we identified the influential authors, journals, countries, institutions, topics and disciplines, key articles and research trends by collaboration network analysis, keyword co-occurrence network and document co-citation network analysis. As a result, we found that environmental sciences technology and water resources were the most popular research subfields, followed by energy fuels and meteorological atmospheric sciences. While as time goes on, research focuses have gradually shifted. Public environmental occupational health will become one of the most popular research subfields in the future. • We review papers of Bayesian networks in the subfields of climate change. • Influential journals, institutions, landmark papers, citation bursts are identified. • Recent topics focus on environmental sciences technology. • Public environmental occupational health will become new hot issues. [ABSTRACT FROM AUTHOR]

Computational models for understanding and predicting fire in wildland and managed lands are increasing in impact. Data characterizing the fuels and environment is needed to continue improvement in the fidelity and reliability of fire outcomes. This paper addresses a gap in the characterization and population of mid-story fuels, which are not easily observable either through traditional survey, where data collection is time consuming, or with remote sensing, where the mid-story is typically obscured by forest canopy. We present a methodology to address populating a mid-story using a generative model for fuel placement that captures key concepts of spatial density and heterogeneity that varies by regional or local environmental conditions. The advantage of using a parameterized generative model is the ability to calibrate (or 'tune') the generated fuels based on comparison to limited observation datasets or with expert guidance, and we show how this generative model can balance information from these sources to capture the essential characteristics of the wildland fuels environment. In this paper we emphasize the connection of terrestrial LiDAR (TLS) as the observations used to calibrate of the generative model, as TLS is a promising method for supporting forest fuels assessment. Code for the methods in this paper is available. • A spatial model generates representative mid-story fuels for wildland fire simulation. • Model parameters are learned to match observed spatial heterogeneity and density. • Terrestrial LiDAR plot observations are used for characterization. • Practical aspects of calibrating stochastic models to limited data are presented. • Spatial covariates inform fuel placement for an application dataset. [ABSTRACT FROM AUTHOR]

This paper presents a dynamical neural network framework to understand how catchment systems respond to daily rainfall-runoff processes over time. We developed an interactive Python-based deep neural network (DNN) package called FlowDyn (presented through a JS-based web platform) to simulate and forecast daily streamflow data for >180 gauging stations across the globe. Several DNN models, including long short-term memory (LSTM), Gated Recurrent Unit (GRU), and a hybrid network of convolutional neural network and LSTM (ConvLSTM), as well as an auto encoder (AE) model were developed and integrated into the FlowDyn pipeline to analyze and forecast sequential daily streamflow values that are embedded within a web-based application for demonstration and visualization. Inputs were gathered from different web services, including the catchment attributes and meteorology for large-sample studies (CAMELS), the national climatic data center (NCDC), and the global runoff data center (GRDC). DNN configurations were trained and tested with an average accuracy rating of 0.83 across 183 river basins globally. FlowDyn simulation and performance demonstrated that different DNN models were able to learn both regionally consistent and location-specific hydrological behaviors. Through the findings of this paper, we advocate the merit of applying FlowDyn package in the field of daily rainfall-runoff prediction at both local and global scales. • A dynamic framework was developed to predict daily streamflow records of >180 gauging stations across the globe. • The FlowDyn pipeline offers functionalities, such as employing multiple DNNs, various performance metrics, and visualization. • FlowDyn has the ability to retrain and provide more accurate results in case of extending the datasets. [ABSTRACT FROM AUTHOR]

This paper presents a pattern language for developing Object-Oriented Bayesian Networks (OOBN), as a member of the component-based probabilistic models family, to tackle complex problems. The proposed pattern language integrates knowledge from various domains, such as modeling, software engineering, and Bayesian networks, to provide a comprehensive solution for developing OOBNs. The paper also provides a validation framework to evaluate the pattern language. As a practical application for the OOBN pattern language, a case study of using it to develop an OOBN is presented. The model in the case study aims to represent the complex interconnections among the Sustainable Development Goals (SDG), long-term sustainability and resilience. The results of the case study validate the effectiveness of the pattern language and highlight its potential for future applications. The proposed OOBN pattern language provides a systematic approach to the development of OOBN, reducing the complexity and increasing the efficiency of their modeling process • New pattern language for Object-Oriented Bayesian Networks. • The pattern language Integrates multidisciplinary knowledge to handle large networks. • Validation framework for language evaluation. • Case study models SDG, sustainability, and nation resilience. [ABSTRACT FROM AUTHOR]

Marine plastic pollution has in recent decades become ubiquitous, posing threats to flora, fauna, and potentially human health. Proper monitoring and registration of litter occurrences are, therefore, of paramount importance to support better-informed decision-making. In this paper, we introduce the PlastOPol marine litter monitoring system. PlastOPol integrates external data sources on beached litter with data collected through citizen science initiatives based on the use of a mobile application (App). The App relies on state-of-the-art machine-learning approaches for litter detection and registration. The system also supports a human-in-the-loop strategy based on which improved versions of litter detection models are created over time thanks to annotations by citizen scientists. Finally, the system includes a geographic visualization tool to support the analysis of litter distribution data by decision-makers. This system has the potential to create a direct path between citizens, researchers, and decision-makers on the issue of marine litter. Finally, the paper presents compelling usage scenarios of the proposed monitoring system and discusses the evaluation of the App through a user study. The user study suggests that the PlastOPol system is an effective and valuable tool to monitor and communicate marine litter. [Display omitted] • This paper introduces PlastOPol, a new system for marine litter monitoring based on citizen science. • This paper introduces a data model for litter occurrence registration and data sharing. • The model evolves based on the annotation of citizen scientists made through a mobile application. • This paper introduces a map-based visualization that supports the analysis of litter distribution. • This paper presents and discusses a user study to assess the potential of PlastOPol to support citizen science tasks. [ABSTRACT FROM AUTHOR]

This paper introduces the web application-type Graphical User Interface that has been developed and also presents an application example. The introduced simulator conducts hydrodynamics and ecosystems in coastal and estuarine areas. It consists of (1) a hydrodynamic model that can simulate the current velocity, water temperature, salinity, and water level; (2) an ecosystem model that can simulate dissolved oxygen, phytoplankton, zooplankton, nutrients, fish, and bivalves; and (3) a benthic ecosystem model that can simulate elution. Web GUI is the first web system of aquatic environment simulation system that can both prepare calculation conditions and visualize them. Another significant feature is that it requires no installation and can be easily used by anyone to perform calculations. Thus, the proposed system helps fill the expertise gap experienced by potential users of the model. The use of standard systems, such as those discussed in this study, will facilitate evidence-based policymaking (EBPM). [Display omitted] • An integrated ecological hydrodynamics simulation system, EcoPARI, was developed. • EcoPARI-Web GUI can conduct simulation from pre-to post-processing via web browser. • EcoPARI-Web GUI requires no installation and can be easily used by anyone. • EcoPARI helps fill the expertise gap experienced by potential users of the model. • The proposed system is expected to contribute to evidence-based policymaking. [ABSTRACT FROM AUTHOR]

The Delaware River Basin (DRB) in the Mid-Atlantic region of the United States is an institutionally complex water resources system that provides drinking water for 13.5 million people, plus water for energy, industry, recreation, and ecosystems. This paper introduces Pywr-DRB, an open-source Python model exploring the impacts of reservoir operations, transbasin diversions, and minimum flow targets on water availability and drought risk in the DRB. Pywr-DRB draws on streamflow estimates from emerging data resources, bridging advances in large-scale hydrologic modeling with an improved representation of the basin's evolving water infrastructure and management institutions. Our detailed model diagnostic assessment demonstrates that Pywr-DRB provides substantial improvements over sole use of hydrologic models in capturing the DRB's dynamics. We also explore how water management alters model-derived risk estimates for low flows and water demand shortfalls. Our approach to diagnostic benchmarking and water systems modeling is broadly applicable to other major basins. • Pywr-DRB enables open science & drought risk planning for the Delaware River Basin. • Pywr-DRB bridges hydrologic model data, observations & water management modeling. • Reservoirs, diversions & minimum flow targets shape low-flow behaviors in the basin. • Reliability of minimum flows & transbasin diversions are strongly interdependent. • Water management modeling is critical for accurate water availability assessment. [ABSTRACT FROM AUTHOR]

Effectively balancing carbon emission reduction with economic viability through regional forest management is a significant challenge for global ecosystems. This paper introduces an innovative multi-modal forest decision-making system, integrating deep learning and natural language processing technologies, aimed at optimizing forest management strategies. Experimental validation of this system was conducted in three distinct forested regions. Utilizing a deep learning model, the system analyzed and predicted daily carbon emissions data. The experiments demonstrated remarkable accuracy, with the model achieving a coefficient of determination (R 2) of up to 0.94, 0.98, and 0.99 across datasets from all three regions, thereby justifying its use for forecasting carbon emission trends over the following months. Subsequently, the system employed natural language processing to assess the importance of various collected forest management strategies. Finally, the system fine-tuned these strategy combinations in response to the predicted carbon emission trends, ensuring flexibility and effectiveness in addressing the complex dynamics of carbon emission fluctuations. • A new multi-modal decision-making model integrates carbon emissions and management strategies in forest management. • Carbon emission datasets from diverse regions across three continents were analyzed using deep learning models. • An evaluation method identified forest management strategies balancing ecological sustainability with economic viability. [ABSTRACT FROM AUTHOR]

Water level monitoring in lakes and reservoirs is essential for effective water resource management, especially in remote areas where traditional ground sensors are costly and difficult to maintain. Remote sensing offers an alternative, but improving the quality, resolution, and accuracy of satellite data remains crucial. This paper introduces MoRLa (Measurement of Reservoir Level from Altimetry), a data filtering procedure designed to enhance satellite altimetry retrievals. MoRLa increases the acceptance of satellite observations and improves the quality of water level estimates by using physical characteristics of water bodies to exclude non-conforming measurements. Unlike previous studies with static masks, MoRLa employs a dynamic filter adaptable to actual water levels at specific times. Tested on reservoirs in the Korean Peninsula, including the Hwang-Gang dam, MoRLa shows significant improvements in water level measurements using Cryosat-2, ICESat-2, and Sentinel-3A and B satellites. [Display omitted] • Introduced MoRLa for improved water level monitoring. • Enhanced accuracy with dynamic water masks. • Reliable data collection during extreme weather. • Benefits global inland water bodies management. • Significant advancements in satellite altimetry application. [ABSTRACT FROM AUTHOR]

In this paper, a time-explicit Finite-Volume method is adopted to solve the 2-D shallow water equations on an unstructured triangular mesh, using a two-stage Runge-Kutta integrator and a monotone MUSCL model to achieve second-order accuracy in time and space, respectively. A multi-GPU model is presented that uses the Message Passing Interface (MPI) with OpenACC and uses the METIS library to produce the domain decomposition. A CUDA-aware MPI library (GPUDirect) and overlapped MPI communication with computation are used to improve parallel performance. Two benchmark tests with wet and dry downstream beds are used to test the code's accuracy. Good results were achieved compared to the numerical simulations of published studies. Compared with the multi-CPU version of a 6-core CPU, maximum speedups of 56.18 and 331.51 were obtained using a single GPU and 8 GPUs, respectively. Higher mesh resolution enhances acceleration performance, and the model is applicable to other environmental modeling activities. • We expressed the shallow-water equations for a 2D area for numerical solution via parallel computation using MPI and OpenACC. • Various parallel computing techniques are optimized to improve the code's performance. • The single-GPU version obtained a speedup factor of 56.18 compared to the 6-core multi-CPU version. • A speedup of 331.51 was achieved by the multi-GPU version over the 6-core workstation processor version. [ABSTRACT FROM AUTHOR]

Accurate representation of river channel geometry is important for hydrologic and hydraulic modeling of fluvial systems. Often, channel geometry is estimated using simple rating curves that can be applied across various spatial scales. However, such methods are limited to power law relations that do not employ many potentially relevant catchment and river attributes. This paper introduce a new dataset, IFMHA (Inventory of Field Measurement of Hydraulic Attributes), to enable research studies on channel geometry and streamflow characteristics. IFMHA is derived from the National Water Information System (NWIS) site inventory for surface water field measurements and stream attributes from the National Hydrography Dataset (NHD). IFMHA includes 2,802,532 records from 10,050 sites (NWIS streamgaging stations). The dataset utility is demonstrated here by presenting a series of conceptual models for estimating channel geometry parameters (i.e., channel mean depth, channel maximum depth, wetted perimeter, and roughness) based on the available field attributes within IFMHA. Such a dataset and attributed channel geometry parameters can enhance the performance of operational flood forecasting frameworks (e.g. National Water Model) by providing more accurate initial conditions used in hydrologic and hydraulic routing models. • A comprehensive dataset with more observations aids data-intensive models. • IFMHA offers stream traits: mean/max depth, wetted perimeter, roughness for shapes. • Channel geometry variations minimally affect Manning's roughness coefficient values. [ABSTRACT FROM AUTHOR]

Currently, the deep geological repository approach for spent nuclear fuel is regarded as the most dependable and secure method for permanently disposing of this kind of waste. Among its key safety components is an engineered barrier made from compacted bentonite, which isolates the encapsulated waste from the surrounding host rock. As a result, understanding how bentonites react to varying compositions of groundwater is crucial. This is where numerical modelling becomes essential. It is generally approved by the scientific community to idealise bentonite as a material structured under a double porosity system composed of the macro and microstructure. In this context, this paper illustrates the capabilities of a double-porosity reactive transport model for bentonites fully implemented in the multiphysics COMSOL platform. For this purpose, different experimental tests were simulated based on the evaluation of diffusive ion transport, mineral dissolution and cation exchange processes in MX-80 bentonite, obtaining very satisfactory results. • A new reactive transport model for bentonites was implemented in COMSOL Multiphysics. • The model includes a geochemical system composed of 14 chemical species and gypsum. • The model considers diffusive-dispersive-advective transport in double-porosity media. • The model validation was run by 3 tests set in the Task Force on Engineered Barrier. [ABSTRACT FROM AUTHOR]