Evaluating Climatic Warming and the Modulating Effects of Surface Water and Regional Variables in Western Bangladesh

Results in Engineering, Год журнала: 2025, Номер unknown, С. 103864 - 103864

Опубликована: Янв. 1, 2025

Язык: Английский

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Attribution of the Climate and Land Use Change Impact on the Hydrological Processes of Athabasca River Basin, Canada

Hydrology, Год журнала: 2025, Номер 12(1), С. 7 - 7

Опубликована: Янв. 7, 2025

Climate change (CC) and land use/land cover (LULCC) are significant drivers of hydrological change, an effective watershed management requires a detailed understanding their individual the combined impact. This study focused on Athabasca River Basin (ARB), Canada, investigated how basin responded to changes using MIKE SHE-MIKE Hydro River. Our findings revealed novel insights into ARB changes, including increment in non-vegetated lands (0.26%), savannas (1.28%), forests (0.53%), urban areas (0.02%) while grasslands (2.07%) shrublands (0.03%) decreased. Moreover, experienced rising annual minimum (1.01 °C) maximum (0.85 temperatures but declining precipitation (6.2%). The suggested impact CC compared LULCC as caused reduction streamflow (7.9%), evapotranspiration (4.8%), recharge (6.9%). Meanwhile, reduced (0.2%) (0.4%) increased (0.1%). spatiotemporal variability across ARB, with temperature impacts stronger winter influencing other seasons.

Язык: Английский

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Comparison of Perimeter Delineation Methods for Remote Sensing Fire Spot Data in Near/Ultra-Real-Time Applications

Fire, Год журнала: 2024, Номер 7(7), С. 226 - 226

Опубликована: Июль 1, 2024

Forest fires cause extensive damage to ecosystems, biodiversity, and human property, posing significant challenges for emergency response resource management. The accurate timely delineation of forest fire perimeters is crucial mitigating these impacts. In this study, methods delineating using near-real-time (NRT) remote sensing data are evaluated. Specifically, the performance various algorithms—buffer, concave, convex, combination methods—using VIIRS MODIS datasets assessed. It was found that increasing concave α values improves matching percentage with reference areas but also increases commission error (CE), indicating overestimation. results demonstrate generally achieve higher percentages, CEs. These findings highlight trade-off between improved perimeter accuracy risk insights gained optimizing sensor alignment techniques, thereby enhancing rapid response, allocation, evacuation planning in This research first employ multiple algorithms both individual synergistic approaches NRT or ultra-real-time (URT) active data, providing a critical foundation future studies aimed at improving timeliness assessments. Such advancements essential effective disaster management mitigation strategies.

Язык: Английский

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Active Fire Clustering and Spatiotemporal Dynamic Models for Forest Fire Management

Fire, Год журнала: 2024, Номер 7(10), С. 355 - 355

Опубликована: Окт. 6, 2024

Forest fires are increasingly destructive, contributing to significant ecological damage, carbon emissions, and economic losses. Monitoring these promptly accurately, particularly by delineating fire perimeters, is critical for mitigating their impact. Satellite-based remote sensing, especially using active products from VIIRS MODIS, has proven indispensable real-time forest monitoring. Despite advancements, challenges remain in accurately clustering perimeters a timely manner, as many existing methods rely on manual processing, resulting delays. Active perimeter (AFP) Timely Fire Progression (TAFP) models were developed which aim be an automated approach data points perimeters. The results demonstrated that the combined dataset achieved highest matching rate of 85.13% across all size classes, with 95.95% accuracy ≥100 ha. However, decreased smaller fires. Overall, 1500 m radii alpha values 0.1 found most effective delineation, when applied at larger radii. proposed can play role improving operational responses management agencies, helping mitigate destructive impact more effectively.

Язык: Английский

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A multidimensional machine learning framework for LST reconstruction and climate variable analysis in forest fire occurrence

Ecological Informatics, Год журнала: 2024, Номер 83, С. 102849 - 102849

Опубликована: Окт. 12, 2024

Язык: Английский

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