Land surface temperature responses to land use dynamics in urban areas of Doha, Qatar

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 104, P. 105273 - 105273

Published: Feb. 13, 2024

Rapid urbanization primarily converts naturally vegetated areas and pervious surfaces into impervious built-up areas, significantly transforming microclimates ecological dynamics. The surfaces, marked by their higher thermal conductivity, disrupt surface energy balance accumulate solar heat, subsequently elevating the land temperatures (LSTs). This study investigates impact of use cover changes on summer winter LSTs in Doha Al Dayeen municipalities Qatar, spanning from years 2000 to 2023, using remote sensing techniques Geographic Information Systems (GIS). analysis reveals a remarkable 343.16% increase area at expense previously existing desert lands water bodies. While Qatar's has high temperature, substituting such with exhibits notable rise temperatures. Additionally, reclamation also results elevated LSTs. LST data derived sources demonstrates an upward trend for contrasting winter. Specifically, mean increases 7.64°C (0.34°C annually), decreases 4.87°C (0.22°C annually). Notably, consistently recorded highest both seasons all observed years. A strong correlation was between patterns Normalized Difference Vegetation Index (NDVI), Water (NDWI), Built-up index (NDBI) Barrenness (NDBal). imply negative influence climate change urgent need urban planning mitigation measures counteract adverse effects increasing LSTs, particularly months, ensure human well-being resilience environments.

Language: Английский

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Minimizing uncertainties in climate projections and water budget reveals the vulnerability of freshwater to climate change

One Earth, Journal Year: 2024, Volume and Issue: 7(1), P. 72 - 87

Published: Jan. 1, 2024

Global water scarcity threatens agriculture, food security, and human sustainability. Hence, understanding changes in terrestrial storage (WS) is crucial. By utilizing climate models, reanalysis, satellite data, we demonstrate the effectiveness of multivariate bias correction technique facilitating precise WS representation while ensuring robust budget closure. Historical data indicate seasonal changes, where forested basins exhibit a surplus December-January-February season, with reversal June-July-August-September season. Non-forested display varied patterns influenced by geographical location land use type. Future projections increased deficits most Southern Hemisphere under middle-road (SSP 245) scenario wetter conditions regional rivalry 370) scenario. Weather systems governing vary season basin, resulting inconsistent moisture intake into basins. These findings underscore intricate interplay between transport, characteristics, WS, highlighting need to understand these complex interactions for effective resource management strategies changing climates.

Language: Английский

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Global vegetation, moisture, thermal and climate interactions intensify compound extreme events

The Science of The Total Environment, Journal Year: 2023, Volume and Issue: 912, P. 169261 - 169261

Published: Dec. 12, 2023

Compound extreme events, encompassing drought, vegetation stress, wildfire severity, and heatwave intensity (CDVWHS), pose significant threats to societal, environmental, health systems. Understanding the intricate relationships governing CDVWHS evolution their interaction with climate teleconnections is crucial for effective adaptation strategies. This study leverages remote sensing, reanalysis data, models analyze during historical (1982–2014), near-future (2028–2060), far-future (2068–2100) periods under two Shared Socioeconomic Pathways (SSP; 245 585). Our results show that reduced health, unfavorable temperature conditions, low moisture conditions have negligible effects on density. However, they worsen of heatwaves increase risk wildfires. Wildfires can persist when thermal are poor despite favorable levels. For example, adequate availability, we link 2012 Siberian in Ob basin anomalous negative concurrent thermal-moisture conditions. In contrast, Amazon experiences exceptional drought associated same year. A comparative analysis North American fires reveals distinct burned area anomalies due variations density fuel. The lower positive areas because density, which amount Furthermore, examine basin-specific variability related compound CDVWHS, revealing primary modes through teleconnection patterns. Moreover, a substantial magnitude severity emerges between near far future SSP 585. underscores urgency targeted actions enhance ecosystem resilience safeguard vulnerable communities from impacts. Identifying hotspots comprehending complex environmental factors essential developing strategies changing climate.

Language: Английский

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A novel dual-layer composite framework for downscaling urban land surface temperature coupled with spatial autocorrelation and spatial heterogeneity

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2024, Volume and Issue: 130, P. 103900 - 103900

Published: May 19, 2024

Land surface temperature (LST) captures fundamental information on the spatiotemporal variation of energy balance at surface. The trade-off between spatial and temporal resolutions remote sensing images (retrieved LSTs), however, restricts fine-scale thermal environmental investigations. In this context, a novel dual-layer composite framework (DCF) for LST downscaling coupling autocorrelation heterogeneity was developed based two laws geography used to improve existing kernel-driven methods. Besides, new non-parametric method (N-DLST) also proposed under DCF, in which Bayesian general regression (BNGR) applied predict high-resolution LSTs with auto-selected kernels. experiment Landsat 8 from 300 m 30 over highly heterogeneous urban area, N-DLST significantly outperformed original methods, highest coefficient determination (R2 = 0.93) lowest root mean square error (RMSE 0.85). Moreover, enhanced effects DCF were demonstrated by comparing accuracy disaggregation radiometric (DisTrad), geographically weighted regression-based (GWR), random forest (RF) before after their improvements. Visual interpretation quantitative assessments revealed that could DisTrad, GWR, RF methods an increase R2 approximately 0.09 decrease RMSE more than 0.4 °C. cases contexts water bodies, effectively preserved textures large-scale variations, yielding most consistent pattern reference LST. Given simplicity modelling process absence auxiliary data, strengthen performance both linear nonlinear while serve as effective tool prediction.

Language: Английский

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Revealing the roles of climate, urban form, and vegetation greening in shaping the land surface temperature of urban agglomerations in the Yangtze River Economic Belt of China

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 377, P. 124602 - 124602

Published: Feb. 20, 2025

Language: Английский

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Hourly impact of urban features on the spatial distribution of land surface temperature: A study across 30 cities

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 113, P. 105701 - 105701

Published: July 27, 2024

Language: Английский

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Climate change is intensifying rainfall erosivity and soil erosion in West Africa

The Science of The Total Environment, Journal Year: 2024, Volume and Issue: 955, P. 177174 - 177174

Published: Nov. 2, 2024

Soil erosion is a critical environmental challenge with significant implications for agriculture, water quality, and ecosystem stability. Understanding its dynamics essential sustainable management societal welfare. Here, we analyze rainfall erosivity patterns across West Africa (WAF) during the historical (1982-2014), near future (2028-2060), far (2068-2100) periods under Shared Socioeconomic Pathways (SSPs 370 585). Using bias-corrected-downscaled (BCD) climate models validated against reference data, ensure an accurate representation of rainfall-a key driver (R-factor) soil erosion. We compare Renard's approach Modified Fournier Index (MFI) to calculate R-factor note strong correlation. However, method shows slightly lower accuracy in Sierra Leone, Guinea, The Gambia, likely due inability capture high-intensity, short-duration events. In contrast, MFI, utilizing continuous rain gauge proves more reliable these regions. also attribute fluctuations erosivity, such as those seen 2003 floods, synoptic weather influenced by multiple processes. Furthermore, our analysis reveals regions where could exceed 20 t/ha/yr change. Under SSP scenario, WAF projected rise 14.84 % 18.65 future, increasing further 585 19.86 23.49 %, respectively. most severe increases are expected Benin Nigeria, Nigeria potentially facing 66.41 585. These findings highlight region's exposure intensified climatic conditions underscore urgent need targeted adaptation strategies mitigate erosion's ecological socioeconomic impacts.

Language: Английский

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Climatic scenario-based integrated recurrent ensemble model for energy demand forecasting

Energy and Buildings, Journal Year: 2024, Volume and Issue: 310, P. 114103 - 114103

Published: March 19, 2024

Language: Английский

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Spatio-Temporal Evolution of Olive Tree Water Status Using Land Surface Temperature and Vegetation Indices Derived from Landsat 5 and 8 Satellite Imagery in Southern Peru

Agriculture, Journal Year: 2024, Volume and Issue: 14(5), P. 662 - 662

Published: April 25, 2024

Land surface temperature (LST) and its relationship with vegetation indices (VIs) have proven to be effective for monitoring water stress in large-scale crops. Therefore, the objective of this study is find an appropriate VI analyse spatio-temporal evolution olive using LST images VIs derived from Landsat 5 8 satellites semi-arid region southern Peru. For purpose, (Normalised Difference Vegetation Index (NDVI), Enhanced 2 (EVI2) Soil Adjusted (SAVI)) were calculated. The information was processed Google Earth Engine (GEE) period 1985 2024, interval every five years summer season. triangle method applied based on LST-VIs scatterplot analysis, a tool that establishes wet dry boundary conditions Temperature Dryness (TVDI). results indicated better appreciation orchard over time, average 39% drought (TVDINDVI TVDISAVI), 24% severe (TVDINDVI) 25% (TVDISAVI) total area, compared TVDIEVI2, which showed 37% 16% drought. It concluded TVDINDVI TVDISAVI provide visualisation map crop offer range options address current future problems resource management sector areas

Language: Английский

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Machine learning and remote sensing based time series analysis for drought risk prediction in Borena Zone, Southwest Ethiopia

Journal of Arid Environments, Journal Year: 2024, Volume and Issue: 222, P. 105160 - 105160

Published: April 11, 2024

Language: Английский

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