scholarly journals GLOFRIM v1.0 – A globally applicable computational framework for integrated hydrological-hydrodynamic modelling

2017 ◽  
Author(s):  
Jannis M. Hoch ◽  
Jeffrey C. Neal ◽  
Fedor Baart ◽  
Rens van Beek ◽  
Hessel C. Winsemius ◽  
...  
Keyword(s):  
Large Scale ◽  
Distributed Model ◽  
Free Access ◽  
Test Case ◽  
Hydrodynamic Modelling ◽  
Hydrodynamic Models ◽  
Computational Framework ◽  
Entire Study ◽  
Regular Grids ◽  
Inundation Extent

Abstract. To increase the representation of physical processes in inundation modelling, current research approaches aim to integrate both hydrological and hydrodynamic models. A previous study by Hoch et al. (2017) showed that spatially explicit coupling approaches can outperform stand-alone runs by single-purpose models as they combine spatially distributed model forcing by hydrological models with more sophisticated routing schemes in hydrodynamic models. We here present GLOFRIM, a globally applicable computational framework for integrated hydrological-hydrodynamic modelling, to facilitate such coupling approaches and to cater for an ensemble of models to be coupled. It currently allows for coupling the global hydrological model PCR-GLOBWB with either Delft3D Flexible Mesh (DFM), solving the full shallow-water equations and allowing for spatially flexible meshing, or LISFLOOD-FP (LFP), solving the local inertia equations and running on regular grids. The main advantages of the framework are its open and free access, its global applicability, its versatility, and its extensibility with other hydrological or hydrodynamic models. Before applying GLOFRIM to an actual test case, we benchmarked both DFM and LFP for a synthetic test case. Results show that for sub-critical flow conditions, discharge response to the same input signal is near identical for both models, which agrees with previous studies. We subsequently applied the framework to the Amazon River basin to test the framework thoroughly and, in addition, to perform a first-ever benchmark of flexible and regular grids at the large-scale. Both DFM and LFP produce comparable results in terms of simulated discharge with LFP exhibiting slightly higher accuracy as expressed by a Kling-Gupta-Efficiency of 0.82 compared to 0.76 for DFM. However, benchmarking inundation extent between DFM and LFP over the entire study area, a critical success index of 0.46 was obtained, indicating that the models disagree as often as they agree. Differences between models in both simulated discharge and inundation extent is to a large extent attributable to the gridding techniques employed. In fact, the result show that the numerical scheme of the inundation model and the gridding technique can contribute as strongly to deviations in simulated inundation extent as, unlike the global flood model inter-comparison by Trigg et al. (2016), we control for model forcing and boundary conditions. This study shows that the presented computational framework is robust and widely applicable. GLOFRIM is designed as open access and to be easily extendable, and thus we hope that other large-scale hydrological and hydrodynamic models will be added, eventually capturing more locally relevant processes as well as allowing for more robust model inter-comparison, benchmarking, and ensemble simulations of flood hazard at the large scale.

scholarly journals GLOFRIM v1.0 – A globally applicable computational framework for integrated hydrological–hydrodynamic modelling

2017 ◽  
Vol 10 (10) ◽  
pp. 3913-3929 ◽  
Author(s):  
Jannis M. Hoch ◽  
Jeffrey C. Neal ◽  
Fedor Baart ◽  
Rens van Beek ◽  
Hessel C. Winsemius ◽  
...  
Keyword(s):  
Large Scale ◽  
Flood Hazard ◽  
Free Access ◽  
Test Case ◽  
Hydrodynamic Modelling ◽  
Hydrodynamic Models ◽  
Computational Framework ◽  
Entire Study ◽  
Regular Grids ◽  
Inundation Extent

Abstract. We here present GLOFRIM, a globally applicable computational framework for integrated hydrological–hydrodynamic modelling. GLOFRIM facilitates spatially explicit coupling of hydrodynamic and hydrologic models and caters for an ensemble of models to be coupled. It currently encompasses the global hydrological model PCR-GLOBWB as well as the hydrodynamic models Delft3D Flexible Mesh (DFM; solving the full shallow-water equations and allowing for spatially flexible meshing) and LISFLOOD-FP (LFP; solving the local inertia equations and running on regular grids). The main advantages of the framework are its open and free access, its global applicability, its versatility, and its extensibility with other hydrological or hydrodynamic models. Before applying GLOFRIM to an actual test case, we benchmarked both DFM and LFP for a synthetic test case. Results show that for sub-critical flow conditions, discharge response to the same input signal is near-identical for both models, which agrees with previous studies. We subsequently applied the framework to the Amazon River basin to not only test the framework thoroughly, but also to perform a first-ever benchmark of flexible and regular grids on a large-scale. Both DFM and LFP produce comparable results in terms of simulated discharge with LFP exhibiting slightly higher accuracy as expressed by a Kling–Gupta efficiency of 0.82 compared to 0.76 for DFM. However, benchmarking inundation extent between DFM and LFP over the entire study area, a critical success index of 0.46 was obtained, indicating that the models disagree as often as they agree. Differences between models in both simulated discharge and inundation extent are to a large extent attributable to the gridding techniques employed. In fact, the results show that both the numerical scheme of the inundation model and the gridding technique can contribute to deviations in simulated inundation extent as we control for model forcing and boundary conditions. This study shows that the presented computational framework is robust and widely applicable. GLOFRIM is designed as open access and easily extendable, and thus we hope that other large-scale hydrological and hydrodynamic models will be added. Eventually, more locally relevant processes would be captured and more robust model inter-comparison, benchmarking, and ensemble simulations of flood hazard on a large scale would be allowed for.

scholarly journals High-Performance Integrated hydrodynamic Modelling of Storm Induced Floods at a Catchment Scale

10.29007/tt1x ◽  
2018 ◽  
Author(s):  
Xilin Xia ◽  
Qiuhua Liang ◽  
Xiaodong Ming
Keyword(s):  
Real Time ◽  
High Performance ◽  
Large Scale ◽  
Hydrodynamic Modelling ◽  
Hydrological Models ◽  
Graphic Processing Units ◽  
Catchment Scale ◽  
Promising Tool ◽  
Inundation Extent ◽  
The Uk

Flooding is one of the most common types of natural hazards. The current practice of large-scale fluvial flood modelling relies on the use of hydrological models to predict upstream discharge hydrograph to drive inundation modelling at downstream. However, the oversimplified representation of both catchment topographies and hydraulics make hydrological models heavily rely on model parameterisation and calibration. This makes the modelling strategy unsuitable for prediction of extreme events featured with highly transient hydraulic processes, for which high-quality hydrological data is commonly missing to support model parameterisation and calibration. In this paper, the High-Performance Integrated hydrodynamic Modelling System (HiPIMS) has been adapted and applied to the whole 2500 km2 Eden catchment in the UK to reproduce the flood event caused by Storm Desmond in December, 2015. Without necessity of intensive calibration and using hydrographs as boundary conditions, satisfactory results have been obtained for both inundation extent and water level time series in channels, in comparison with observations. Accelerated by multiple modern graphic processing units (GPUs), the model runs more than 20 times faster than real time for the simulation of the whole catchment at 20m resolution. The results successfully demonstrate HiPIMS as a promising tool for real-time flood forecasting and flood risk assessment.

scholarly journals PRISMS-Fatigue computational framework for fatigue analysis in polycrystalline metals and alloys

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Mohammadreza Yaghoobi ◽  
Krzysztof S. Stopka ◽  
Aaditya Lakshmanan ◽  
Veera Sundararaghavan ◽  
John E. Allison ◽  
...  
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Large Scale ◽  
Metals And Alloys ◽  
Analysis Tool ◽  
Computational Framework ◽  
Crystal Plasticity Finite Element ◽  
Polycrystalline Metals ◽  
Simulation Based ◽  
Open Source Framework

AbstractThe PRISMS-Fatigue open-source framework for simulation-based analysis of microstructural influences on fatigue resistance for polycrystalline metals and alloys is presented here. The framework uses the crystal plasticity finite element method as its microstructure analysis tool and provides a highly efficient, scalable, flexible, and easy-to-use ICME community platform. The PRISMS-Fatigue framework is linked to different open-source software to instantiate microstructures, compute the material response, and assess fatigue indicator parameters. The performance of PRISMS-Fatigue is benchmarked against a similar framework implemented using ABAQUS. Results indicate that the multilevel parallelism scheme of PRISMS-Fatigue is more efficient and scalable than ABAQUS for large-scale fatigue simulations. The performance and flexibility of this framework is demonstrated with various examples that assess the driving force for fatigue crack formation of microstructures with different crystallographic textures, grain morphologies, and grain numbers, and under different multiaxial strain states, strain magnitudes, and boundary conditions.

scholarly journals A large-scale 2005–2012 flood map record derived from ENVISAT-ASAR data: United Kingdom as a test case

2021 ◽  
Vol 256 ◽  
pp. 112338
Author(s):  
Jie Zhao ◽  
Ramona Pelich ◽  
Renaud Hostache ◽  
Patrick Matgen ◽  
Wolfgang Wagner ◽  
...  
Keyword(s):  
United Kingdom ◽  
Large Scale ◽  
Test Case ◽  
Envisat Asar

scholarly journals FAIRSCAPE: a Framework for FAIR and Reproducible Biomedical Analytics

2021 ◽  
Author(s):  
Maxwell Adam Levinson ◽  
Justin Niestroy ◽  
Sadnan Al Manir ◽  
Karen Fairchild ◽  
Douglas E. Lake ◽  
...  
Keyword(s):  
Computational Result ◽  
Large Scale ◽  
Graph Model ◽  
Inferential Reasoning ◽  
Computational Framework ◽  
Textual Description ◽  
Evidence Graph ◽  
Computational Analyses ◽  
Processing Steps ◽  
Multiple Processing

AbstractResults of computational analyses require transparent disclosure of their supporting resources, while the analyses themselves often can be very large scale and involve multiple processing steps separated in time. Evidence for the correctness of any analysis should include not only a textual description, but also a formal record of the computations which produced the result, including accessible data and software with runtime parameters, environment, and personnel involved. This article describes FAIRSCAPE, a reusable computational framework, enabling simplified access to modern scalable cloud-based components. FAIRSCAPE fully implements the FAIR data principles and extends them to provide fully FAIR Evidence, including machine-interpretable provenance of datasets, software and computations, as metadata for all computed results. The FAIRSCAPE microservices framework creates a complete Evidence Graph for every computational result, including persistent identifiers with metadata, resolvable to the software, computations, and datasets used in the computation; and stores a URI to the root of the graph in the result’s metadata. An ontology for Evidence Graphs, EVI (https://w3id.org/EVI), supports inferential reasoning over the evidence. FAIRSCAPE can run nested or disjoint workflows and preserves provenance across them. It can run Apache Spark jobs, scripts, workflows, or user-supplied containers. All objects are assigned persistent IDs, including software. All results are annotated with FAIR metadata using the evidence graph model for access, validation, reproducibility, and re-use of archived data and software.

scholarly journals Assessing the impact of hydrodynamics on large-scale flood wave propagation – a case study for the Amazon Basin

2017 ◽  
Vol 21 (1) ◽  
pp. 117-132 ◽  
Author(s):  
Jannis M. Hoch ◽  
Arjen V. Haag ◽  
Arthur van Dam ◽  
Hessel C. Winsemius ◽  
Ludovicus P. H. van Beek ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Hydrodynamic Model ◽  
Large Scale ◽  
Hydrologic Model ◽  
Flood Events ◽  
Coupled Models ◽  
Validation Parameters ◽  
Directional Coupling ◽  
Inundation Extent ◽  
The Impact

Abstract. Large-scale flood events often show spatial correlation in neighbouring basins, and thus can affect adjacent basins simultaneously, as well as result in superposition of different flood peaks. Such flood events therefore need to be addressed with large-scale modelling approaches to capture these processes. Many approaches currently in place are based on either a hydrologic or a hydrodynamic model. However, the resulting lack of interaction between hydrology and hydrodynamics, for instance, by implementing groundwater infiltration on inundated floodplains, can hamper modelled inundation and discharge results where such interactions are important. In this study, the global hydrologic model PCR-GLOBWB at 30 arcmin spatial resolution was one-directionally and spatially coupled with the hydrodynamic model Delft 3D Flexible Mesh (FM) for the Amazon River basin at a grid-by-grid basis and at a daily time step. The use of a flexible unstructured mesh allows for fine-scale representation of channels and floodplains, while preserving a coarser spatial resolution for less flood-prone areas, thus not unnecessarily increasing computational costs. In addition, we assessed the difference between a 1-D channel/2-D floodplain and a 2-D schematization in Delft 3D FM. Validating modelled discharge results shows that coupling PCR-GLOBWB to a hydrodynamic routing scheme generally increases model performance compared to using a hydrodynamic or hydrologic model only for all validation parameters applied. Closer examination shows that the 1-D/2-D schematization outperforms 2-D for r2 and root mean square error (RMSE) whilst having a lower Kling–Gupta efficiency (KGE). We also found that spatial coupling has the significant advantage of a better representation of inundation at smaller streams throughout the model domain. A validation of simulated inundation extent revealed that only those set-ups incorporating 1-D channels are capable of representing inundations for reaches below the spatial resolution of the 2-D mesh. Implementing 1-D channels is therefore particularly of advantage for large-scale inundation models, as they are often built upon remotely sensed surface elevation data which often enclose a strong vertical bias, hampering downstream connectivity. Since only a one-directional coupling approach was tested, and therefore important feedback processes are not incorporated, simulated discharge and inundation extent for both coupled set-ups is generally overpredicted. Hence, it will be the subsequent step to extend it to a two-directional coupling scheme to obtain a closed feedback loop between hydrologic and hydrodynamic processes. The current findings demonstrating the potential of one-directionally and spatially coupled models to obtain improved discharge estimates form an important step towards a large-scale inundation model with a full dynamic coupling between hydrology and hydrodynamics.

Revisiting Tourism Destination Image: A Holistic Measurement Framework Using Big Data

2021 ◽  
pp. 004728752110247
Author(s):  
Vinh Bui ◽  
Ali Reza Alaei ◽  
Huy Quan Vu ◽  
Gang Li ◽  
Rob Law
Keyword(s):  
Large Scale ◽  
Structural Model ◽  
Destination Image ◽  
Tourism Destination ◽  
Computational Framework ◽  
Four Dimensions ◽  
Tourism Management ◽  
Measurement Framework ◽  
Compare And Contrast

Understanding and being able to measure, analyze, compare, and contrast the image of a tourism destination, also known as tourism destination image (TDI), is critical in tourism management and destination marketing. Although various methodologies have been developed, a consistent, reliable, and scalable method for measuring TDI is still unavailable. This study aims to address the challenge by proposing a framework for a holistic measure of TDI in four dimensions, including popularity, sentiment, time, and location. A structural model for TDI measurement that covers various aspects of a tourism destination is developed. TDI is then measured by a comprehensive computational framework that can analyze complex textual and visual data on a large scale. A case study using more than 30,000 images, and 10,000 comments in relation to three tourism destinations in Australia demonstrates the effectiveness of the proposed framework.

Comparison of Various Discretization Schemes for Simulation of Large Field Case Reservoirs Using Unstructured Grids

2021 ◽  
Author(s):  
Samier Pierre ◽  
Raguenel Margaux ◽  
Darche Gilles
Keyword(s):  
Large Scale ◽  
Unstructured Grids ◽  
Computational Cost ◽  
Large Field ◽  
Real Field ◽  
Test Case ◽  
Generation Task ◽  
Field Case ◽  
Flux Approximation ◽  
Discretization Schemes

Abstract Solving the equations governing multiphase flow in geological formations involves the generation of a mesh that faithfully represents the structure of the porous medium. This challenging mesh generation task can be greatly simplified by the use of unstructured (tetrahedral) grids that conform to the complex geometric features present in the subsurface. However, running a million-cell simulation problem using an unstructured grid on a real, faulted field case remains a challenge for two main reasons. First, the workflow typically used to construct and run the simulation problems has been developed for structured grids and needs to be adapted to the unstructured case. Second, the use of unstructured grids that do not satisfy the K-orthogonality property may require advanced numerical schemes that preserve the accuracy of the results and reduce potential grid orientation effects. These two challenges are at the center of the present paper. We describe in detail the steps of our workflow to prepare and run a large-scale unstructured simulation of a real field case with faults. We perform the simulation using four different discretization schemes, including the cell-centered Two-Point and Multi-Point Flux Approximation (respectively, TPFA and MPFA) schemes, the cell- and vertex-centered Vertex Approximate Gradient (VAG) scheme, and the cell- and face-centered hybrid Mimetic Finite Difference (MFD) scheme. We compare the results in terms of accuracy, robustness, and computational cost to determine which scheme offers the best compromise for the test case considered here.

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