Spatiotemporal variations and driving factors of evapotranspiration in the Yunnan-Guizhou Plateau from 2003 to 2020 DOI Creative Commons

S. Chen,

Bo‐Hui Tang, Xianguang Ma

et al.

Journal of Water and Climate Change, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 15, 2024

ABSTRACT Evapotranspiration (ET) is vital for the Earth's energy and water balance, particularly influenced by global climate change. The Yunnan-Guizhou Plateau (YGP), characterized abundant resources intricate terrain, has been a subject of study. However, previous research often overlooked intra-annual variations in ET. This study employed high-spatiotemporal-resolution ET data from 2003 to 2020 quantitatively analyze spatiotemporal characteristics on YGP. annual showed an increasing trend 0.18 mm/year, with monthly increases January, March, November, December, mainly vegetation transpiration, which accounts 56% Breakpoints trends seasonal components occurred January 2007 June 2018. geodetector model assessed impact 15 driving factors ET, net radiation index playing dominant roles q-values 0.29 0.24. Factor impacts varied seasonally, greater influence dry season (q-value 0.53 January) less rainy 0.08 August). Pearson correlation analysis indicated that different months. These findings enhance understanding plateau responses climate-change mechanisms.

Language: Английский

Revolutionizing the Future of Hydrological Science: Impact of Machine Learning and Deep Learning amidst Emerging Explainable AI and Transfer Learning DOI Creative Commons
Rajib Maity, Aman Srivastava,

Subharthi Sarkar

et al.

Applied Computing and Geosciences, Journal Year: 2024, Volume and Issue: 24, P. 100206 - 100206

Published: Nov. 9, 2024

Language: Английский

Citations

6
Enhancing the prediction of irrigation demand for open field vegetable crops in Germany through neural networks, transfer learning, and ensemble models DOI Creative Commons
Samantha Rubo, Jana Zinkernagel

Agricultural Water Management, Journal Year: 2025, Volume and Issue: 312, P. 109402 - 109402

Published: March 18, 2025

Language: Английский

Citations

0
Comparative analysis of machine learning models for rainfall prediction DOI

Pritee Krishna Das,

Rajiv Lochan Sahu,

Prakash Chandra Swain

et al.

Journal of Atmospheric and Solar-Terrestrial Physics, Journal Year: 2024, Volume and Issue: 264, P. 106340 - 106340

Published: Aug. 30, 2024

Language: Английский

Citations

0
Spatiotemporal variations and driving factors of evapotranspiration in the Yunnan-Guizhou Plateau from 2003 to 2020 DOI Creative Commons

S. Chen,

Bo‐Hui Tang, Xianguang Ma

et al.

Journal of Water and Climate Change, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 15, 2024

ABSTRACT Evapotranspiration (ET) is vital for the Earth's energy and water balance, particularly influenced by global climate change. The Yunnan-Guizhou Plateau (YGP), characterized abundant resources intricate terrain, has been a subject of study. However, previous research often overlooked intra-annual variations in ET. This study employed high-spatiotemporal-resolution ET data from 2003 to 2020 quantitatively analyze spatiotemporal characteristics on YGP. annual showed an increasing trend 0.18 mm/year, with monthly increases January, March, November, December, mainly vegetation transpiration, which accounts 56% Breakpoints trends seasonal components occurred January 2007 June 2018. geodetector model assessed impact 15 driving factors ET, net radiation index playing dominant roles q-values 0.29 0.24. Factor impacts varied seasonally, greater influence dry season (q-value 0.53 January) less rainy 0.08 August). Pearson correlation analysis indicated that different months. These findings enhance understanding plateau responses climate-change mechanisms.

Language: Английский

Citations

0