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The spatial and temporal variation and uncertainty of precipitation and runoff in China were compared and evaluated between historical and future periods under different climate change scenarios. The precipitation pattern is derived from observed and future projected precipitation data for historical and future periods, respectively. The runoff is derived from simulation results in historical and future periods using a dynamic global vegetation model (DGVM) forced with historical observed and global climate models (GCMs) future projected climate data, respectively. One GCM (CGCM3.1) under two emission scenarios (SRES A2 and SRES B1) was used for the future period simulations. The results indicated high uncertainties and variations in climate change effects on hydrological processes in China: precipitation and runoff showed a significant increasing trend in the future period but a decreasing trend in the historical period at the national level; the temporal variation and uncertainty of projected precipitation and runoff in the future period were predicted to be higher than those in the historical period; the levels of precipitation and runoff in the future period were higher than those in the historical period. The change in trends of precipitation and runoff are highly affected by different climate change scenarios. GCM structure and emission scenarios should be the major sources of uncertainty.
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Alongside global warming, droughts are expected to increase in frequency, severity, and extent in the near future, which will likely result in significant impacts on forest growth, production, structure, composition, and ecosystem services. However, due to spatial and temporal characteristics, it is difficult to monitor and assess the potential effects of droughts. Remote sensing can provide an effective way to obtain real-time conditions of forests affected by drought and offer a range of spatial and temporal insights into drought-induced changes to forest ecosystem structure, function, and services. Remote sensing is rapidly developing as more satellites are launched. In situ and remotely sensed data fusion techniques have achieved notable success in assessing drought-induced damage to forests and carbon cycles. Even so, constraints still exist when using satellite data. The objectives of this review are to (1) briefly review existing data sources and methods of remote sensing; (2) synthesize current applications and contributions of remote sensing in monitoring and estimating impacts of droughts on forest ecosystems; and (3) highlight research gaps and future challenges.
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Abstract Sources of methane ( CH 4 ) become highly variable for countries undergoing a heightened period of development due to both human activity and climate change. An urgent need therefore exists to budget key sources of CH 4 , such as wetlands (rice paddies and natural wetlands) and lakes (including reservoirs and ponds), which are sensitive to these changes. For this study, references in relation to CH 4 emissions from rice paddies, natural wetlands, and lakes in C hina were first reviewed and then reestimated based on the review itself. Total emissions from the three CH 4 sources were 11.25 Tg CH 4 yr −1 (ranging from 7.98 to 15.16 Tg CH 4 yr −1 ). Among the emissions, 8.11 Tg CH 4 yr −1 (ranging from 5.20 to 11.36 Tg CH 4 yr −1 ) derived from rice paddies, 2.69 Tg CH 4 yr −1 (ranging from 2.46 to 3.20 Tg CH 4 yr −1 ) from natural wetlands, and 0.46 Tg CH 4 yr −1 (ranging from 0.33 to 0.59 Tg CH 4 yr −1 ) from lakes (including reservoirs and ponds). Plentiful water and warm conditions, as well as its large rice paddy area make rice paddies in southeastern C hina the greatest overall source of CH 4 , accounting for approximately 55% of total paddy emissions. Natural wetland estimates were slightly higher than the other estimates owing to the higher CH 4 emissions recorded within Q inghai‐ T ibetan P lateau peatlands. Total CH 4 emissions from lakes were estimated for the first time by this study, with three quarters from the littoral zone and one quarter from lake surfaces. Rice paddies, natural wetlands, and lakes are not constant sources of CH 4 , but decreasing ones influenced by anthropogenic activity and climate change. A new progress‐based model used in conjunction with more observations through model‐data fusion approach could help obtain better estimates and insights with regard to CH 4 emissions deriving from wetlands and lakes in C hina.