Building subsurface velocity models is essential to our goals in utilizing seismic data for Earth discovery and exploration, as well as monitoring. With the dawn of machine learning, these velocity models (or, more precisely, their distribution) can be stored accurately and efficiently in a generative model. These stored velocity model distributions can be utilized to regularize or quantify uncertainties in inverse problems, like full waveform inversion. However, most generators, like normalizing flows or diffusion models, treat the image (velocity model) uniformly, disregarding spatial dependencies and resolution changes with respect to the observation locations. To address this weakness, we introduce VelocityGPT, a novel implementation that utilizes Transformer decoders trained autoregressively to generate a velocity model from shallow subsurface to deep. Owing to the fact that seismic data are often recorded on the Earth's surface, a top-down generator can utilize the inverted information in the shallow as guidance (prior) to generating the deep. To facilitate the implementation, we use an additional network to compress the velocity model. We also inject prior information, like well or structure (represented by a migration image) to generate the velocity model. Using synthetic data, we demonstrate the effectiveness of VelocityGPT as a promising approach in generative model applications for seismic velocity model building.
{"title":"Propagating the prior from shallow to deep with a pre-trained velocity-model Generative Transformer network","authors":"Randy Harsuko, Shijun Cheng, Tariq Alkhalifah","doi":"arxiv-2408.09767","DOIUrl":"https://doi.org/arxiv-2408.09767","url":null,"abstract":"Building subsurface velocity models is essential to our goals in utilizing\u0000seismic data for Earth discovery and exploration, as well as monitoring. With\u0000the dawn of machine learning, these velocity models (or, more precisely, their\u0000distribution) can be stored accurately and efficiently in a generative model.\u0000These stored velocity model distributions can be utilized to regularize or\u0000quantify uncertainties in inverse problems, like full waveform inversion.\u0000However, most generators, like normalizing flows or diffusion models, treat the\u0000image (velocity model) uniformly, disregarding spatial dependencies and\u0000resolution changes with respect to the observation locations. To address this\u0000weakness, we introduce VelocityGPT, a novel implementation that utilizes\u0000Transformer decoders trained autoregressively to generate a velocity model from\u0000shallow subsurface to deep. Owing to the fact that seismic data are often\u0000recorded on the Earth's surface, a top-down generator can utilize the inverted\u0000information in the shallow as guidance (prior) to generating the deep. To\u0000facilitate the implementation, we use an additional network to compress the\u0000velocity model. We also inject prior information, like well or structure\u0000(represented by a migration image) to generate the velocity model. Using\u0000synthetic data, we demonstrate the effectiveness of VelocityGPT as a promising\u0000approach in generative model applications for seismic velocity model building.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211736","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Erico Kutchartt, José Ramón González-Olabarria, Núria Aquilué, Jordi Garcia-Gonzalo, Antoni Trasobares, Brigite Botequim, Marius Hauglin, Palaiologos Palaiologou, Vassil Vassilev, Adrian Cardil, Miguel Ángel Navarrete, Christophe Orazio, Francesco Pirotti
Canopy fuels and surface fuel models, topographic features and other canopy attributes such as stand height and canopy cover, provide the necessary spatial datasets required by various fire behaviour modelling simulators. This is a technical note reporting on a pan-European fuel map server, highlighting the methods for the production and validation of canopy features, more specifically canopy fuels, and surface fuel models created for the European Union Horizon 2020 FIRE-RES project, as well as other related data derived from earth observation. The aim was to deliver a fuel cartography in a findable, accessible, interoperable and replicable manner as per F.A.I.R. guiding principles for research data stewardship. We discuss the technology behind sharing large raster datasets via web-GIS technologies and highlight advances and novelty of the shared data. Uncertainty maps related to the canopy fuel variables are also available to give users the expected reliability of the data. Users can view, query and download single layers of interest, or download the whole pan-European dataset. All layers are in raster format and co-registered in the same reference system, extent and spatial resolution (100 m). Viewing and downloading is available at all NUTS scales, ranging from country level (NUTS0) to province level (NUTS3), thus facilitating data management and access. The system was implemented using R for part of the processing and Google Earth Engine. The final app is openly available to the public for accessing the data at various scales.
{"title":"Pan-European fuel map server: an open-geodata portal for supporting fire risk assessment","authors":"Erico Kutchartt, José Ramón González-Olabarria, Núria Aquilué, Jordi Garcia-Gonzalo, Antoni Trasobares, Brigite Botequim, Marius Hauglin, Palaiologos Palaiologou, Vassil Vassilev, Adrian Cardil, Miguel Ángel Navarrete, Christophe Orazio, Francesco Pirotti","doi":"arxiv-2409.00008","DOIUrl":"https://doi.org/arxiv-2409.00008","url":null,"abstract":"Canopy fuels and surface fuel models, topographic features and other canopy\u0000attributes such as stand height and canopy cover, provide the necessary spatial\u0000datasets required by various fire behaviour modelling simulators. This is a\u0000technical note reporting on a pan-European fuel map server, highlighting the\u0000methods for the production and validation of canopy features, more specifically\u0000canopy fuels, and surface fuel models created for the European Union Horizon\u00002020 FIRE-RES project, as well as other related data derived from earth\u0000observation. The aim was to deliver a fuel cartography in a findable,\u0000accessible, interoperable and replicable manner as per F.A.I.R. guiding\u0000principles for research data stewardship. We discuss the technology behind\u0000sharing large raster datasets via web-GIS technologies and highlight advances\u0000and novelty of the shared data. Uncertainty maps related to the canopy fuel\u0000variables are also available to give users the expected reliability of the\u0000data. Users can view, query and download single layers of interest, or download\u0000the whole pan-European dataset. All layers are in raster format and\u0000co-registered in the same reference system, extent and spatial resolution (100\u0000m). Viewing and downloading is available at all NUTS scales, ranging from\u0000country level (NUTS0) to province level (NUTS3), thus facilitating data\u0000management and access. The system was implemented using R for part of the\u0000processing and Google Earth Engine. The final app is openly available to the\u0000public for accessing the data at various scales.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The main hurdle of planet formation theory is the metre-scale barrier. One of the most promising ways to overcome it is via the streaming instability (SI). Unfortunately, the mechanism responsible for the onset of this instability remains mysterious. It has recently been shown that the SI is a Resonant Drag Instability (RDI) involving inertial waves. We build on this insight and clarify the physical picture of how the SI develops, while bolstering this picture with transparent mathematics. Like all RDIs, the SI is built on a feedback loop: in the `forward action', an inertial wave concentrates dust into clumps; in the `backward reaction', those drifting dust clumps excite an inertial wave. Each process breaks into two mechanisms, a fast one and a slow one. At resonance, each forward mechanism can couple with a backward mechanism to close a feedback loop. Unfortunately, the fast-fast loop is stable, so the SI uses the fast-slow and slow-fast loops. Despite this last layer of complexity, we hope that our explanation will help understand how the SI works, in which conditions it can grow, how it manifests itself, and how it saturates.
行星形成理论的主要障碍是米级壁垒。不幸的是，导致这种不稳定性发生的机制仍然很神秘。最近的研究表明，流不稳定性是一种涉及惯性波的共振阻力不稳定性（RDI）。我们以这一见解为基础，阐明了 SI 如何发展的物理图景，同时用透明的数学来支持这一图景。与所有 RDI 一样，SI 建立在反馈回路之上：在 "前向作用 "中，惯性波将尘埃集中成团；在 "后向反应 "中，这些漂移的尘埃团块激发惯性波。每个过程都分为快速和慢速两种机制。在共振时，每个前向机制都可以与一个后向机制耦合，从而形成一个反馈回路。不幸的是，"快-快 "环路是稳定的，因此，SI 使用了 "快-慢 "和 "慢-快 "环路。尽管还有最后一层复杂性，但我们希望我们的解释将有助于理解 SI 的工作原理、它在哪些条件下可以增长、它是如何表现出来的以及它是如何达到饱和的。
{"title":"The physical mechanism of the streaming instability","authors":"Nathan Magnan, Tobias Heinemann, Henrik N. Latter","doi":"arxiv-2408.07441","DOIUrl":"https://doi.org/arxiv-2408.07441","url":null,"abstract":"The main hurdle of planet formation theory is the metre-scale barrier. One of\u0000the most promising ways to overcome it is via the streaming instability (SI).\u0000Unfortunately, the mechanism responsible for the onset of this instability\u0000remains mysterious. It has recently been shown that the SI is a Resonant Drag\u0000Instability (RDI) involving inertial waves. We build on this insight and\u0000clarify the physical picture of how the SI develops, while bolstering this\u0000picture with transparent mathematics. Like all RDIs, the SI is built on a\u0000feedback loop: in the `forward action', an inertial wave concentrates dust into\u0000clumps; in the `backward reaction', those drifting dust clumps excite an\u0000inertial wave. Each process breaks into two mechanisms, a fast one and a slow\u0000one. At resonance, each forward mechanism can couple with a backward mechanism\u0000to close a feedback loop. Unfortunately, the fast-fast loop is stable, so the\u0000SI uses the fast-slow and slow-fast loops. Despite this last layer of\u0000complexity, we hope that our explanation will help understand how the SI works,\u0000in which conditions it can grow, how it manifests itself, and how it saturates.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Esteban Andrés Cúñez Benalcázar, Erick de Moraes Franklin
Barchans are crescent-shape dunes ubiquitous on Earth and other celestial bodies, which are organized in barchan fields where they interact with each other. Over the last decades, satellite images have been largely employed to detect barchans on Earth and on the surface of Mars, with AI (Artificial Intelligence) becoming an important tool for monitoring those bedforms. However, automatic detection reported in previous works is limited to isolated dunes and does not identify successfully groups of interacting barchans. In this paper, we inquire into the automatic detection and tracking of barchans by carrying out experiments and exploring the acquired images using AI. After training a neural network with images from controlled experiments where complex interactions took place between dunes, we did the same for satellite images from Earth and Mars. We show, for the first time, that a neural network trained properly can identify and track barchans interacting with each other in different environments, using different image types (contrasts, colors, points of view, resolutions, etc.), with confidence scores (accuracy) above 70%. Our results represent a step further for automatically monitoring barchans, with important applications for human activities on Earth, Mars and other celestial bodies.
{"title":"Detection and tracking of barchan dunes using Artificial Intelligence","authors":"Esteban Andrés Cúñez Benalcázar, Erick de Moraes Franklin","doi":"arxiv-2408.07584","DOIUrl":"https://doi.org/arxiv-2408.07584","url":null,"abstract":"Barchans are crescent-shape dunes ubiquitous on Earth and other celestial\u0000bodies, which are organized in barchan fields where they interact with each\u0000other. Over the last decades, satellite images have been largely employed to\u0000detect barchans on Earth and on the surface of Mars, with AI (Artificial\u0000Intelligence) becoming an important tool for monitoring those bedforms.\u0000However, automatic detection reported in previous works is limited to isolated\u0000dunes and does not identify successfully groups of interacting barchans. In\u0000this paper, we inquire into the automatic detection and tracking of barchans by\u0000carrying out experiments and exploring the acquired images using AI. After\u0000training a neural network with images from controlled experiments where complex\u0000interactions took place between dunes, we did the same for satellite images\u0000from Earth and Mars. We show, for the first time, that a neural network trained\u0000properly can identify and track barchans interacting with each other in\u0000different environments, using different image types (contrasts, colors, points\u0000of view, resolutions, etc.), with confidence scores (accuracy) above 70%. Our\u0000results represent a step further for automatically monitoring barchans, with\u0000important applications for human activities on Earth, Mars and other celestial\u0000bodies.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142226704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Convection driven geodynamo models in rotating spherical geometry have regimes in which reversals occur. However, reversing dynamo models are usually found in regimes where the kinetic and magnetic energy is comparable, so that inertia is playing a significant role in the dynamics. In the Earth's core, the Rossby number is very small, and the magnetic energy is much larger than the kinetic energy. Here we investigate dynamo models in the strong field regime, where magnetic forces have a significant effect on convection. In the core, the strong field is achieved by having the magnetic Prandtl number Pm small, but the Ekman number E extremely small. In simulations, very small E is not possible, but the strong field regime can be reached by increasing Pm. However, if Pm is raised while the fluid Prandtl number is fixed at unity, the most common choice, the Peclet number number becomes small, so that the linear terms in the heat (or composition) equation dominate, which is also far from Earth-like behaviour. Here we increase Pr and Pm together, so that nonlinearity is important in the heat equation and the dynamo is strong field. We find that Earth-like reversals are possible at numerically achievable parameter values, and the simulations have Earth-like magnetic fields away from the times at which it reverses. The magnetic energy is much greater than the kinetic energy except close to reversal times.
{"title":"Low inertia reversing geodynamos","authors":"Chris Jones, Yue-Kin Tsang","doi":"arxiv-2408.07420","DOIUrl":"https://doi.org/arxiv-2408.07420","url":null,"abstract":"Convection driven geodynamo models in rotating spherical geometry have\u0000regimes in which reversals occur. However, reversing dynamo models are usually\u0000found in regimes where the kinetic and magnetic energy is comparable, so that\u0000inertia is playing a significant role in the dynamics. In the Earth's core, the\u0000Rossby number is very small, and the magnetic energy is much larger than the\u0000kinetic energy. Here we investigate dynamo models in the strong field regime,\u0000where magnetic forces have a significant effect on convection. In the core, the\u0000strong field is achieved by having the magnetic Prandtl number Pm small, but\u0000the Ekman number E extremely small. In simulations, very small E is not\u0000possible, but the strong field regime can be reached by increasing Pm. However,\u0000if Pm is raised while the fluid Prandtl number is fixed at unity, the most\u0000common choice, the Peclet number number becomes small, so that the linear terms\u0000in the heat (or composition) equation dominate, which is also far from\u0000Earth-like behaviour. Here we increase Pr and Pm together, so that nonlinearity\u0000is important in the heat equation and the dynamo is strong field. We find that\u0000Earth-like reversals are possible at numerically achievable parameter values,\u0000and the simulations have Earth-like magnetic fields away from the times at\u0000which it reverses. The magnetic energy is much greater than the kinetic energy\u0000except close to reversal times.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Earth's fast rotation imposes the Taylor-Proudman Constraint that opposes fluid motion across an imaginary cylindrical surface called the Tangent Cylinder (TC) obtained by extruding the equatorial perimeter of the solid inner core along the rotation direction, and up to the core-mantle boundary (CMB). To date however, the influence of this boundary is unknown and this impedes our understanding of the flow in the polar regions of the core. We reproduce the TC geometry experimentally, where the CMB is modelled as a cold, cylindrical vessel, with a hot cylinder inside it acting as the inner solid core. The vessel is filled with water so as to optically map the velocity field in regimes of criticality and rotational constraint consistent with those of the Earth. We find that the main new mechanism arises out of the baroclinicity near the cold lateral boundary of the vessel, which drives inertia at the outer boundary of the TC, as convection in the equatorial regions of the Earth's core does. The baroclinicity just outside the TC suppresses the classical wall modes found in solid cylinder and the inertia there causes an early breakup of the TPC at the TC boundary. The flow remains dominated by the Coriolis force even up to criticality $Rt=191$, but because of inertia near the TC boundary, geostrophic turbulence appears at much lower criticality than in other settings. The heat flux escapes increasingly through the TC boundary as the TPC becomes weaker. Hence inertia driven by baroclinicity outside the TC provides a convenient shortcut to geostrophic turbulence, which is otherwise difficult to reach in experiments. These results also highlight a process whereby the convection outside the TC may control turbulence inside it and bypass the axial heat transfer. We finally discuss how Earth's conditions, especially its magnetic field may change how this process acts within the Earth's core.
{"title":"Regimes of rotating convection in an experimental model of the Earth's tangent cylinder","authors":"Rishav Agrawal, Martin Holdsworth, Alban Pothérat","doi":"arxiv-2408.07837","DOIUrl":"https://doi.org/arxiv-2408.07837","url":null,"abstract":"Earth's fast rotation imposes the Taylor-Proudman Constraint that opposes\u0000fluid motion across an imaginary cylindrical surface called the Tangent\u0000Cylinder (TC) obtained by extruding the equatorial perimeter of the solid inner\u0000core along the rotation direction, and up to the core-mantle boundary (CMB). To\u0000date however, the influence of this boundary is unknown and this impedes our\u0000understanding of the flow in the polar regions of the core. We reproduce the TC\u0000geometry experimentally, where the CMB is modelled as a cold, cylindrical\u0000vessel, with a hot cylinder inside it acting as the inner solid core. The\u0000vessel is filled with water so as to optically map the velocity field in\u0000regimes of criticality and rotational constraint consistent with those of the\u0000Earth. We find that the main new mechanism arises out of the baroclinicity near\u0000the cold lateral boundary of the vessel, which drives inertia at the outer\u0000boundary of the TC, as convection in the equatorial regions of the Earth's core\u0000does. The baroclinicity just outside the TC suppresses the classical wall modes\u0000found in solid cylinder and the inertia there causes an early breakup of the\u0000TPC at the TC boundary. The flow remains dominated by the Coriolis force even\u0000up to criticality $Rt=191$, but because of inertia near the TC boundary,\u0000geostrophic turbulence appears at much lower criticality than in other\u0000settings. The heat flux escapes increasingly through the TC boundary as the TPC\u0000becomes weaker. Hence inertia driven by baroclinicity outside the TC provides a\u0000convenient shortcut to geostrophic turbulence, which is otherwise difficult to\u0000reach in experiments. These results also highlight a process whereby the\u0000convection outside the TC may control turbulence inside it and bypass the axial\u0000heat transfer. We finally discuss how Earth's conditions, especially its\u0000magnetic field may change how this process acts within the Earth's core.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"24 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nicolao Cerqueira Lima, Willian Righi Assis, Carlos Azael Alvarez, Erick de Moraes Franklin
Barchans are eolian dunes of crescent shape found on Earth, Mars and other celestial bodies. Among the different types of barchan-barchan interaction, there is one, known as chasing, in which the dunes remain close but without touching each other. In this paper, we investigate the origins of this barchan-barchan dune repulsion by carrying out grain-scale numerical computations in which a pair of granular heaps is deformed by the fluid flow into barchan dunes that interact with each other. In our simulations, data such as position, velocity and resultant force are computed for each individual particle at each time step, allowing us to measure details of both the fluid and grains that explain the repulsion. We show the trajectories of grains, time-average resultant forces, and mass balances for each dune, and that the downstream barchan shrinks faster than the upstream one, keeping, thus, a relatively high velocity although in the wake of the upstream barchan. In its turn, this fast shrinkage is caused by the flow disturbance, which induces higher erosion on the downstream barchan and its circumvention by grains leaving the upstream dune. Our results help explaining the mechanisms behind the distribution of barchans in dune fields found on Earth and Mars.
{"title":"Barchan-barchan dune repulsion investigated at the grain scale","authors":"Nicolao Cerqueira Lima, Willian Righi Assis, Carlos Azael Alvarez, Erick de Moraes Franklin","doi":"arxiv-2408.07604","DOIUrl":"https://doi.org/arxiv-2408.07604","url":null,"abstract":"Barchans are eolian dunes of crescent shape found on Earth, Mars and other\u0000celestial bodies. Among the different types of barchan-barchan interaction,\u0000there is one, known as chasing, in which the dunes remain close but without\u0000touching each other. In this paper, we investigate the origins of this\u0000barchan-barchan dune repulsion by carrying out grain-scale numerical\u0000computations in which a pair of granular heaps is deformed by the fluid flow\u0000into barchan dunes that interact with each other. In our simulations, data such\u0000as position, velocity and resultant force are computed for each individual\u0000particle at each time step, allowing us to measure details of both the fluid\u0000and grains that explain the repulsion. We show the trajectories of grains,\u0000time-average resultant forces, and mass balances for each dune, and that the\u0000downstream barchan shrinks faster than the upstream one, keeping, thus, a\u0000relatively high velocity although in the wake of the upstream barchan. In its\u0000turn, this fast shrinkage is caused by the flow disturbance, which induces\u0000higher erosion on the downstream barchan and its circumvention by grains\u0000leaving the upstream dune. Our results help explaining the mechanisms behind\u0000the distribution of barchans in dune fields found on Earth and Mars.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The application of process-based and data-driven hydrological models is crucial in modern hydrological research, especially for predicting key water cycle variables such as runoff, evapotranspiration (ET), and soil moisture. These models provide a scientific basis for water resource management, flood forecasting, and ecological protection. Process-based models simulate the physical mechanisms of watershed hydrological processes, while data-driven models leverage large datasets and advanced machine learning algorithms. This paper reviewed and compared methods for assessing and enhancing the extrapolability of both model types, discussing their prospects and limitations. Key strategies include the use of leave-one-out cross-validation and similarity-based methods to evaluate model performance in ungauged regions. Deep learning, transfer learning, and domain adaptation techniques are also promising in their potential to improve model predictions in data-sparse and extreme conditions. Interdisciplinary collaboration and continuous algorithmic advancements are also important to strengthen the global applicability and reliability of hydrological models.
{"title":"Approaches for enhancing extrapolability in process-based and data-driven models in hydrology","authors":"Haiyang Shi","doi":"arxiv-2408.07071","DOIUrl":"https://doi.org/arxiv-2408.07071","url":null,"abstract":"The application of process-based and data-driven hydrological models is\u0000crucial in modern hydrological research, especially for predicting key water\u0000cycle variables such as runoff, evapotranspiration (ET), and soil moisture.\u0000These models provide a scientific basis for water resource management, flood\u0000forecasting, and ecological protection. Process-based models simulate the\u0000physical mechanisms of watershed hydrological processes, while data-driven\u0000models leverage large datasets and advanced machine learning algorithms. This\u0000paper reviewed and compared methods for assessing and enhancing the\u0000extrapolability of both model types, discussing their prospects and\u0000limitations. Key strategies include the use of leave-one-out cross-validation\u0000and similarity-based methods to evaluate model performance in ungauged regions.\u0000Deep learning, transfer learning, and domain adaptation techniques are also\u0000promising in their potential to improve model predictions in data-sparse and\u0000extreme conditions. Interdisciplinary collaboration and continuous algorithmic\u0000advancements are also important to strengthen the global applicability and\u0000reliability of hydrological models.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Chris J. Wright, Joel A. Thornton, Lyatt Jaeglé, Yang Cao, Yannian Zhu, Jihu Liu, Randall Jones II, Robert H Holzworth, Daniel Rosenfeld, Robert Wood, Peter Blossey, Daehyun Kim
Aerosol interactions with clouds represent a significant uncertainty in our understanding of the Earth system. Deep convective clouds may respond to aerosol perturbations in several ways that have proven difficult to elucidate with observations. Here, we leverage the two busiest maritime shipping lanes in the world, which emit aerosol particles and their precursors into an otherwise relatively clean tropical marine boundary layer, to make headway on the influence of aerosol on deep convective clouds. The recent seven-fold change in allowable fuel sulfur by the International Maritime Organization allows us to test the sensitivity of the lightning to changes in ship plume aerosol size distributions. We find that, across a range of atmospheric thermodynamic conditions, the previously documented enhancement of lightning over the shipping lanes has fallen by over 40%. The enhancement is therefore at least partially aerosol-mediated, a conclusion that is supported by observations of droplet number at cloud base, which show a similar decline over the shipping lane. These results have fundamental implications for our understanding of aerosol-cloud interactions, suggesting that deep convective clouds are impacted by the aerosol number distribution in the remote marine environment.
{"title":"Lightning declines over shipping lanes following regulation of fuel sulfur emissions","authors":"Chris J. Wright, Joel A. Thornton, Lyatt Jaeglé, Yang Cao, Yannian Zhu, Jihu Liu, Randall Jones II, Robert H Holzworth, Daniel Rosenfeld, Robert Wood, Peter Blossey, Daehyun Kim","doi":"arxiv-2408.07207","DOIUrl":"https://doi.org/arxiv-2408.07207","url":null,"abstract":"Aerosol interactions with clouds represent a significant uncertainty in our\u0000understanding of the Earth system. Deep convective clouds may respond to\u0000aerosol perturbations in several ways that have proven difficult to elucidate\u0000with observations. Here, we leverage the two busiest maritime shipping lanes in\u0000the world, which emit aerosol particles and their precursors into an otherwise\u0000relatively clean tropical marine boundary layer, to make headway on the\u0000influence of aerosol on deep convective clouds. The recent seven-fold change in\u0000allowable fuel sulfur by the International Maritime Organization allows us to\u0000test the sensitivity of the lightning to changes in ship plume aerosol size\u0000distributions. We find that, across a range of atmospheric thermodynamic\u0000conditions, the previously documented enhancement of lightning over the\u0000shipping lanes has fallen by over 40%. The enhancement is therefore at least\u0000partially aerosol-mediated, a conclusion that is supported by observations of\u0000droplet number at cloud base, which show a similar decline over the shipping\u0000lane. These results have fundamental implications for our understanding of\u0000aerosol-cloud interactions, suggesting that deep convective clouds are impacted\u0000by the aerosol number distribution in the remote marine environment.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrea Bondesan, Laurence Girolami, François James, Loïc Rousseau
We introduce a system of Saint-Venant-type equations to model laboratory experiments of dam-break particle-laden flows. We explore homogeneous and non-cohesive liquid-solid suspensions of monodispersed glass beads that propagate as single-phase flows, forming a progressively growing deposit of particles at the bottom of a smooth channel and creating a thin layer of pure liquid at the surface. The novelty of this model is twofold. First, we fully characterize the first-order behavior of these flows (mean velocity, runout distances and deposits geometry) through the sole sedimentation process of the grains, thus avoiding the use of any artificial friction to stop the flow. The model remains very simple and turns out to be effective despite the complex nature of interactions involved in these phenomena. Secondly, the sedimentation dynamics of the grains is observed to not being mainly affected by the flow, but remains comparable to that measured in static suspensions. The mathematical model is validated by comparing the experimental kinematics and deposit profiles with the simulations. The results highlight that this simplified model is sufficient to describe the general features of these flows as well as their deposit morphology, provided that the settling rate is adjusted starting from a critical value of the Reynolds number where the flow agitation begins to significantly delay the mean sedimentation velocity.
{"title":"A three-layer model for the dam-break flow of particulate suspensions driven by sedimentation","authors":"Andrea Bondesan, Laurence Girolami, François James, Loïc Rousseau","doi":"arxiv-2408.06980","DOIUrl":"https://doi.org/arxiv-2408.06980","url":null,"abstract":"We introduce a system of Saint-Venant-type equations to model laboratory\u0000experiments of dam-break particle-laden flows. We explore homogeneous and\u0000non-cohesive liquid-solid suspensions of monodispersed glass beads that\u0000propagate as single-phase flows, forming a progressively growing deposit of\u0000particles at the bottom of a smooth channel and creating a thin layer of pure\u0000liquid at the surface. The novelty of this model is twofold. First, we fully\u0000characterize the first-order behavior of these flows (mean velocity, runout\u0000distances and deposits geometry) through the sole sedimentation process of the\u0000grains, thus avoiding the use of any artificial friction to stop the flow. The\u0000model remains very simple and turns out to be effective despite the complex\u0000nature of interactions involved in these phenomena. Secondly, the sedimentation\u0000dynamics of the grains is observed to not being mainly affected by the flow,\u0000but remains comparable to that measured in static suspensions. The mathematical\u0000model is validated by comparing the experimental kinematics and deposit\u0000profiles with the simulations. The results highlight that this simplified model\u0000is sufficient to describe the general features of these flows as well as their\u0000deposit morphology, provided that the settling rate is adjusted starting from a\u0000critical value of the Reynolds number where the flow agitation begins to\u0000significantly delay the mean sedimentation velocity.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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