Evaluating potential impacts of land use changes on water supply–demand under multiple development scenarios in dryland region

Journal of Hydrology, Journal Year: 2022, Volume and Issue: 610, P. 127811 - 127811

Published: April 13, 2022

Language: Английский

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Drought impacts on hydrology and water quality under climate change

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 858, P. 159854 - 159854

Published: Oct. 30, 2022

Language: Английский

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Pushing the frontiers in climate modelling and analysis with machine learning

Nature Climate Change, Journal Year: 2024, Volume and Issue: 14(9), P. 916 - 928

Published: Aug. 23, 2024

Language: Английский

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Recent Advances in Plant Drought Tolerance

Journal of Plant Growth Regulation, Journal Year: 2024, Volume and Issue: 43(10), P. 3337 - 3369

Published: May 18, 2024

Language: Английский

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Spatiotemporal patterns and driving forces of net primary productivity in South and Southeast Asia based on Google Earth Engine and MODIS data

CATENA, Journal Year: 2025, Volume and Issue: 249, P. 108689 - 108689

Published: Jan. 5, 2025

Language: Английский

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Vulnerability assessment of drought in India: Insights from meteorological, hydrological, agricultural and socio-economic perspectives

Gondwana Research, Journal Year: 2022, Volume and Issue: 123, P. 68 - 88

Published: Nov. 14, 2022

Language: Английский

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Analysis, characterization, prediction, and attribution of extreme atmospheric events with machine learning and deep learning techniques: a review

Theoretical and Applied Climatology, Journal Year: 2023, Volume and Issue: 155(1), P. 1 - 44

Published: Aug. 28, 2023

Abstract Atmospheric extreme events cause severe damage to human societies and ecosystems. The frequency intensity of extremes other associated are continuously increasing due climate change global warming. accurate prediction, characterization, attribution atmospheric is, therefore, a key research field in which many groups currently working by applying different methodologies computational tools. Machine learning deep methods have arisen the last years as powerful techniques tackle problems related events. This paper reviews machine approaches applied analysis, most important extremes. A summary used this area, comprehensive critical review literature ML EEs, provided. has been extended rainfall floods, heatwaves temperatures, droughts, weather fog, low-visibility episodes. case study focused on analysis temperature prediction with DL is also presented paper. Conclusions, perspectives, outlooks finally drawn.

Language: Английский

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A Review of Earth Observation-Based Drought Studies in Southeast Asia

Remote Sensing, Journal Year: 2022, Volume and Issue: 14(15), P. 3763 - 3763

Published: Aug. 5, 2022

Drought is a recurring natural climatic hazard event over terrestrial land; it poses devastating threats to human health, the economy, and environment. Given increasing climate crisis, likely that extreme drought phenomena will become more frequent, their impacts probably be devastating. observations from space, therefore, play key role in dissimilating timely accurate information support early warning management mitigation planning, particularly sparse in-situ data regions. In this paper, we reviewed drought-related studies based on Earth observation (EO) products Southeast Asia between 2000 2021. The results of review indicated publications region are increase, with majority (70%) being undertaken Vietnam, Thailand, Malaysia Indonesia. These countries also accounted for nearly 97% economic losses due extremes. Vegetation indices multispectral optical remote sensing sensors remained primary source monitoring region. Many (~21%) did not provide accuracy assessment mapping products, while precipitation was main validation. We observed positive association spatial extent resolution, suggesting 81% articles focused local national scales. Although there an increase research interest region, challenges remain regarding large-area long time-series measurements, combined approach, machine learning-based prediction, integration multi-sensor (e.g., Landsat Sentinel-2). Satellite EO could substantial part future efforts necessary mitigating challenges, ensuring food security, establishing sustainable preservation environment

Language: Английский

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Modelling agricultural drought: a review of latest advances in big data technologies

Geomatics Natural Hazards and Risk, Journal Year: 2022, Volume and Issue: 13(1), P. 2737 - 2776

Published: Oct. 12, 2022

This article reviews the main recent applications of multi-sensor remote sensing and Artificial Intelligence techniques in multivariate modelling agricultural drought. The study focused mainly on three fundamental aspects, namely descriptive modelling, predictive spatial expected risks vulnerability to Thus, out 417 articles across all studies drought, 226 published from 2010 2022 were analyzed provide a global overview current state knowledge drought using inclusion criteria. objective is review available scientific evidence regarding based joint use geospatial technologies artificial intelligence. analysis different methods used, choice algorithms most relevant variables depending whether they are or models. Criteria such as skill score, given game complexity nature validation data considered draw conclusions. results highlight very heterogeneous original literature. For future studies, addition advances prospects, case comparative appear necessary for an in-depth reproducibility operational applicability approaches proposed temporal HIGHLIGHTSThe components fundamentals discussed.The importance hybrid intelligence models widely discussed improving performance traditional machine learning models.Quantum weakly explored modelling. Therefore, should explore this approach.The major challenge frequency related difference return periods (time-shifted spatially effects).

Language: Английский

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Data-driven models for atmospheric air temperature forecasting at a continental climate region

PLoS ONE, Journal Year: 2022, Volume and Issue: 17(11), P. e0277079 - e0277079

Published: Nov. 3, 2022

Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, environment, irrigation, and agriculture, this parameter describes climate change global warming quite well. Thus, accurate timely forecasting essential because it provides more important information that can be relied for future planning. In study, four Data-Driven Approaches, Support Vector Regression (SVR), Tree (RT), Quantile (QRT), ARIMA, Random Forest (RF), Gradient Boosting (GBR), have been applied to forecast short-, mid-term (daily, weekly) over North America under continental climatic conditions. The time-series data relatively long (2000 2021), 70% of are used model calibration 2015), rest validation. autocorrelation partial functions select best input combination models. quality predicting models evaluated using several statistical measures graphical comparisons. For daily scale, SVR has generated estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R 0.964), Absolute (MAE 2.745°C), Thiels' U-statistics (U 0.127). Besides, study found both RT performed very well in weekly temperature. This discovered duration employed dispersion volatility from month substantially predictive models' efficacy. Furthermore, second scenario conducted randomization method divide into training testing phases. performance much better first one, indicating affects pattern studied station. findings offered technical support generating high-resolution forecasts Methodologies.

Language: Английский

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