Evaluation of Three High-Resolution Satellite and Meteorological Reanalysis Precipitation Datasets over the Yellow River Basin in China DOI Open Access
Meixia Xie, Zhenhua Di, Jianguo Liu

и другие.

Water, Год журнала: 2024, Номер 16(22), С. 3183 - 3183

Опубликована: Ноя. 7, 2024

Recently, Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (IMERG) mission and European Centre Medium-Range Weather Forecasts Reanalysis v5 (ERA5) precipitation datasets have been widely used in remote sensing atmospheric studies, respectively, because of their high accuracy. A dataset 268 site-gauge measurements over Yellow River Basin China was this study to comprehensively evaluate performance three high-resolution products, each with a spatial resolution 0.1°, consisting two satellite-derived datasets, IMERG multisource weighted-ensemble (MSWEP), one ERA5-derived dataset, ERA5-Land. The results revealed that distribution annual closely resembled observed rainfall generally exhibited downward trend from southeast northwest. Among had best at scale, whereas ERA5-Land worst due significant overestimation. Specifically, demonstrated highest correlation coefficient (CC) above 0.8 lowest BIAS root mean square error (RMSE), values most regions 24.79 mm/a less than 100 mm/a, presented RMSE exceeding 500 1265.7 CC below 0.2 regions. At season also across all four seasons, maximum 17.99 summer minimum 0.55 winter. Following IMERG, MSWEP data aligned observations entire area summer, southern spring winter, middle autumn. In addition, Kling–Gupta efficiency (KGE) 0.823 scale KGE (>0.77) seasons among products compared MSWEP, which KEG −2.718 −0.403, respectively. Notably, positive deviation both seasonal scales, other relatively smaller biases.

Язык: Английский

Analysis of vegetation dynamics from 2001 to 2020 in China's Ganzhou rare earth mining area using time series remote sensing and SHAP-enhanced machine learning DOI Creative Commons
Ming Lei, Yuandong Wang, Guangxu Liu

и другие.

Ecological Informatics, Год журнала: 2024, Номер 84, С. 102887 - 102887

Опубликована: Ноя. 9, 2024

Язык: Английский

Процитировано

7
Managing Yellow River Watershed Development and Agricultural Use to Reduce the Environmental Impacts of Flooding, Soil Erosion, Siltation and Pollution DOI Open Access

Kenneth R. Olson,

Wadslin Frenelus

Journal of Water Resource and Protection, Год журнала: 2025, Номер 17(03), С. 196 - 222

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0
Evaluation of Three High-Resolution Satellite and Meteorological Reanalysis Precipitation Datasets over the Yellow River Basin in China DOI Open Access
Meixia Xie, Zhenhua Di, Jianguo Liu

и другие.

Water, Год журнала: 2024, Номер 16(22), С. 3183 - 3183

Опубликована: Ноя. 7, 2024

Recently, Integrated Multi-satellite Retrievals for the Global Precipitation Measurement (IMERG) mission and European Centre Medium-Range Weather Forecasts Reanalysis v5 (ERA5) precipitation datasets have been widely used in remote sensing atmospheric studies, respectively, because of their high accuracy. A dataset 268 site-gauge measurements over Yellow River Basin China was this study to comprehensively evaluate performance three high-resolution products, each with a spatial resolution 0.1°, consisting two satellite-derived datasets, IMERG multisource weighted-ensemble (MSWEP), one ERA5-derived dataset, ERA5-Land. The results revealed that distribution annual closely resembled observed rainfall generally exhibited downward trend from southeast northwest. Among had best at scale, whereas ERA5-Land worst due significant overestimation. Specifically, demonstrated highest correlation coefficient (CC) above 0.8 lowest BIAS root mean square error (RMSE), values most regions 24.79 mm/a less than 100 mm/a, presented RMSE exceeding 500 1265.7 CC below 0.2 regions. At season also across all four seasons, maximum 17.99 summer minimum 0.55 winter. Following IMERG, MSWEP data aligned observations entire area summer, southern spring winter, middle autumn. In addition, Kling–Gupta efficiency (KGE) 0.823 scale KGE (>0.77) seasons among products compared MSWEP, which KEG −2.718 −0.403, respectively. Notably, positive deviation both seasonal scales, other relatively smaller biases.

Язык: Английский

Процитировано

0