中国气象干旱时空特征与混合模型预测

doi:10.12118/j.issn.1000-6060.2025.445

Abstract

Abstract:

To enhance drought prediction accuracy, this study uses Chinese ground meteorological observations from 1980 to 2023 and selects the standardized precipitation evapotranspiration index (SPEI) as the drought indicator. Key predictors were identified through correlation analysis, and a hybrid wavelet transform-long short-term memory (WT-LSTM) model was developed using Theil-Sen Median trend analysis and related methods. Two prediction schemes—single-factor and multifactor—were designed to analyze the spatiotemporal evolution of meteorological drought in China. Results show (1) Spatial trends of factors are uneven; precipitation and potential evapotranspiration generally increase, with precipitation showing an “increase-decrease-increase-decrease” pattern from southeast to northwest, and potential evapotranspiration increasing from northwest to southeast. SPEI trends are negative in 88.87% of areas, indicating widespread drought intensification. (2) The average annual drought duration is mostly 1-2 months, with significant increases in average annual drought severity mainly in northwest, north, and northern northeast China. Trends in average annual drought characteristics exhibit a spatial pattern of higher values in the north and lower values in the south. (3) Regions with long seasonal drought durations in each season do not correspond to high drought intensity; high-value areas of summer drought frequency are widely distributed, while winter drought frequency is lowest. (4) Compared with LSTM, the WT-LSTM model performs better, and for single-factor predictions, the multi-factor approach enhances the ability of the model to represent complex drought patterns, significantly improving prediction performance. (5) Under the hybrid model, single-factor prediction is more suitable for regions with relatively stable climatic drought patterns, while multifactor prediction better captures drought trends in climatically complex areas such as Xinjiang Uygur Autonomous Region and the Qinghai-Xizang Plateau.

Key words: drought forecasting, standardized precipitation evapotranspiration index, discrete wavelet transform, long short-term memory neural network

LIU Yangyang, MAO Kebiao, GUO Zhonghua, YUAN Zijin. Spatiotemporal characteristics and hybrid model prediction of meteorological drought in China[J].Arid Land Geography, 2026, 49(5): 881-893.

Figures/Tables 12

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References 36

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气象站点	纬度/N	经度/E
北京首都国际机场站	40°08′	116°58′
成都双流区站	30°57′	103°94′
哈尔滨站	45°93′	126°56′
乌鲁木齐站	43°78′	87°61′
广州白云国际机场站	23°39′	113°29′
日喀则站	29°25′	88°88′

气象干旱等级	标准化降水蒸散发指数（SPEI）
无旱	SPEI>-0.5
轻旱	-1.0<SPEI≤-0.5
中旱	-1.5<SPEI≤-1.0
重旱	-2.0<SPEI≤-1.5
特旱	SPEI≤-2.0

时态	模型	MAE	MSE	RMSE	RSR	RSD	NSE	相关系数
方案1全部数据	LSTM	0.823	1.112	1.048	0.868	1.024	0.244	0.607
方案1全部数据	WT-LSTM	0.261	0.110	0.328	0.340	0.312	0.882	0.947
春季	LSTM	0.763	0.899	0.939	0.856	0.918	0.265	0.596
春季	WT-LSTM	0.305	0.152	0.378	0.412	0.354	0.822	0.923
夏季	LSTM	0.765	0.891	0.933	0.826	0.904	0.312	0.647
夏季	WT-LSTM	0.302	0.151	0.376	0.403	0.360	0.828	0.922
秋季	LSTM	0.819	1.103	1.035	0.874	0.999	0.230	0.575
秋季	WT-LSTM	0.285	0.134	0.357	0.363	0.338	0.864	0.945
冬季	LSTM	0.658	0.681	0.819	0.832	0.800	0.303	0.607
冬季	WT-LSTM	0.244	0.095	0.302	0.314	0.284	0.898	0.957

时态	模型	MAE	MSE	RMSE	RSR	RSD	NSE	相关系数
方案2全部数据	LSTM	0.647	0.759	0.866	0.710	0.860	0.494	0.724
方案2全部数据	WT-LSTM	0.280	0.150	0.384	0.322	0.376	0.895	0.960
春季	LSTM	0.657	0.760	0.865	0.766	0.855	0.408	0.665
春季	WT-LSTM	0.337	0.282	0.511	0.423	0.489	0.814	0.930
夏季	LSTM	0.599	0.676	0.813	0.682	0.794	0.531	0.754
夏季	WT-LSTM	0.371	0.341	0.570	0.447	0.537	0.793	0.929
秋季	LSTM	0.632	0.790	0.878	0.695	0.863	0.514	0.739
秋季	WT-LSTM	0.308	0.177	0.413	0.323	0.398	0.893	0.963
冬季	LSTM	0.663	0.761	0.863	0.807	0.845	0.342	0.616
冬季	WT-LSTM	0.266	0.137	0.358	0.331	0.342	0.886	0.956

Spatiotemporal characteristics and hybrid model prediction of meteorological drought in China

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