Published: Jan. 1, 2024
Language: Английский
Published: Jan. 1, 2024
Language: Английский
Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 381, P. 125183 - 125183
Published: April 9, 2025
Language: Английский
Citations
0CATENA, Journal Year: 2025, Volume and Issue: 252, P. 108884 - 108884
Published: March 5, 2025
Language: Английский
Citations
0Atmosphere, Journal Year: 2024, Volume and Issue: 15(7), P. 792 - 792
Published: June 30, 2024
Spatial downscaling is an effective way to improve the spatial resolution of precipitation products. However, existing methods often fail adequately consider heterogeneity and complex nonlinearity between surface parameters, resulting in poor performance inaccurate expression regional details. In this study, we propose a model based on geographically neural network weighted regression (GNNWR), which integrates normalized difference vegetation index, digital elevation model, land temperature, slope data address nonlinearity. We explored spatiotemporal trends Sichuan region over past two decades. The results show that GNNWR outperforms common for four distinct seasons, achieving maximum R2 0.972 minimum RMSE 3.551 mm. Overall, Province exhibits significant increasing trend from 2001 2019, with distribution pattern low northwest high southeast. downscaled exhibit strongest correlation observed provide more accurate representation patterns. Our findings suggest practical method considering its accuracy performance.
Language: Английский
Citations
1Published: Jan. 1, 2024
Language: Английский
Citations
0