13 citations as recorded by crossref.

1. SMLFire1.0: a stochastic machine learning (SML) model for wildfire activity in the western United States J. Buch et al. 10.5194/gmd-16-3407-2023
2. Drought- and heatwave-associated compound extremes: A review of hotspots, variables, parameters, drivers, impacts, and analysis frameworks M. Afroz et al. 10.3389/feart.2022.914437
3. Spatiotemporal patterns of net primary productivity of subtropical forests in China and its response to drought S. Yin et al. 10.1016/j.scitotenv.2023.169439
4. Compound droughts and hot extremes: Characteristics, drivers, changes, and impacts Z. Hao et al. 10.1016/j.earscirev.2022.104241
5. Identifying compound weather drivers of forest biomass loss with generative deep learning M. Anand et al. 10.1017/eds.2024.2
6. Extreme and compound ocean events are key drivers of projected low pelagic fish biomass N. Le Grix et al. 10.1111/gcb.16968
7. Nonlinear effects of agricultural drought on vegetation productivity in the Yellow River Basin, China Y. Ding et al. 10.1016/j.scitotenv.2024.174903
8. Impacts of Compound Hot–Dry Events on Vegetation Productivity over Northern East Asia J. Kang et al. 10.3390/f15030549
9. The KNMI Large Ensemble Time Slice (KNMI–LENTIS) L. Muntjewerf et al. 10.5194/gmd-16-4581-2023
10. Large Variability in Simulated Response of Vegetation Composition and Carbon Dynamics to Variations in Drought‐Heat Occurrence E. Tschumi et al. 10.1029/2022JG007332
11. The effect of afforestation on a regional carbon sink: a case study in the Yangtze River Delta, China Y. Zhao et al. 10.1088/1748-9326/ad6e08
12. Integration of tree hydraulic processes and functional impairment to capture the drought resilience of a semiarid pine forest D. Nadal-Sala et al. 10.5194/bg-21-2973-2024
13. Impacts of increasing compound hot-dry events on vegetation under the warming-wetting trend in Northwest China Z. Liu et al. 10.1016/j.geosus.2024.08.003