Elevation-driven biases in seasonal weather forecasts: Insights from the Alpine region

Physics and Chemistry of the Earth Parts A/B/C, Год журнала: 2025, Номер unknown, С. 103957 - 103957

Опубликована: Май 1, 2025

Язык: Английский

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Digital Twin Smart Water Conservancy: Status, Challenges, and Prospects

Water, Год журнала: 2024, Номер 16(14), С. 2038 - 2038

Опубликована: Июль 18, 2024

Digital twin technology, a new type of digital technology emerging in recent years, realizes real-time simulation, prediction and optimization by digitally modeling the physical world, providing idea method for design, operation management water conservancy projects, which is great significance realization transformation informatization to intelligent conservancy. In view this, this paper systematically discusses concept development history smart conservancy, compares its differences with traditional models, further proposes five-dimensional model. Based on model research progress summarized focusing six aspects, namely data perception, transmission, analysis processing, construction, interaction collaboration service application, challenges problems application Finally, trend direction technological breakthroughs are envisioned, aiming provide reference guidance field promote field.

Язык: Английский

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Enhancing Watershed Management Through Advanced Geospatial and Morphometric Approaches for Wainganga River Basin, Central India

Springer geography, Год журнала: 2025, Номер unknown, С. 563 - 598

Опубликована: Янв. 1, 2025

Язык: Английский

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Design and Performance Verification of Deep Learning-Based River Flood Prediction System Design and Digital Twin-Based Its Application

Mathematics, Год журнала: 2025, Номер 13(11), С. 1696 - 1696

Опубликована: Май 22, 2025

This paper presents a digital twin-based river management and flood prediction system designed for hydrological environments, including volcanic geology. To address the problems of rapid runoff complex terrain, deep learning-based hybrid model is proposed that integrates Convolutional Neural Network (CNN) spatial feature extraction Recurrent (RNN) with Long Short-Term Memory (LSTM) units temporal sequence modeling. The performance evaluation results show CNN-RNN outperforms individual CNN RNN baselines. achieves macro-average precision 0.97, recall 0.99, an F1 score 0.98, significantly outperforming existing methods. also integrated 3D twin visualization platform to enable real-time monitoring data-driven decision-making.

Язык: Английский

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Water Whiplash in Mediterranean Regions of the World

Water, Год журнала: 2024, Номер 16(3), С. 450 - 450

Опубликована: Янв. 30, 2024

The presence of weather and water whiplash in Mediterranean regions the world is analyzed using historical streamflow records from 1926 to 2023, depending on region. Streamflow United States (California), Italy, Australia, Chile, South Africa publicly available databases. Water whiplash—or rapid shift wet dry periods—are compared. Wet periods are defined based annual deviations record average, occurs when there an abrupt change that overcomes accommodated deficit or surplus. Of all stations, more years (56%) than (44%) these regions, along with similarities variances shifts extremes (i.e., whiplash). On 35% were as countries, highest levels US where 42–53% years. influence El Niño–Southern Oscillation (ENSO) influences Chile strongest during first quarter year. This study found smaller extreme larger less prevalent regions. has implications for management adaptation climate considered.

Язык: Английский

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TwinEco: A Unified Framework for Dynamic Data-Driven Digital Twins in Ecology

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2024, Номер unknown

Опубликована: Июль 24, 2024

Abstract The burgeoning interest in digital twin (DT) technology presents a transformative potential for ecological modelling, offering new ways to model the complex dynamics of ecosystems. This paper introduces TwinEco framework, designed mitigate fragmentation development and deployment DT applications ecology. Compared traditional modelling frameworks, emphasises modularity flexibility by introducing “layers” “components” DTs, accommodating diverse without necessitating all components. modular approach ensures adaptability scalability, promoting interoperability integration with broader initiatives like Destination Earth. Digital twins ecology offer significant advancements over approaches explicitly changing processes states time, integrating extensive data, enabling real-time feedback loops actuating events or policies “real-world”. framework’s capacity adjust environmental conditions enhances its predictive accuracy responsiveness. highlights necessity unified framework prevent divergent interpretations ensure across domains. Future recommendations include expanding case studies demonstrate applicability, assessing Dynamic Data-Driven Application Systems (DDDAS) paradigm within exploring interactions between components optimise performance. Emphasising model-data fusion fostering shared terminology community are crucial success. aims provide robust foundation twins, timely, data-driven decision-making address global challenges.

Язык: Английский

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Developing an open-source flood forecasting system adapted to data-scarce regions: A digital twin coupled with hydrologic-hydrodynamic simulations

Journal of Hydrology, Год журнала: 2024, Номер 644, С. 131929 - 131929

Опубликована: Сен. 8, 2024

Язык: Английский

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