Wetland methane (CH 4 ) emissions are the largest natural source in the global CH 4 budget, contributing to roughly one third of total natural and anthropogenic emissions. As the second most important anthropogenic greenhouse gas in the atmosphere after CO 2 , CH 4 is strongly associated with climate feedbacks. However, due to the paucity of data, wetland CH 4 feedbacks were not fully assessed in the Intergovernmental Panel on Climate Change Fifth Assessment Report. The degree to which future expansion of wetlands and CH 4 emissions will evolve and consequently drive climate feedbacks is thus a question of major concern. Here we present an ensemble estimate of wetland CH 4 emissions driven by 38 general circulation models for the 21st century. We find that climate changeinduced increases in boreal wetland extent and temperature-driven increases in tropical CH 4 T errestrial wetlands are among the largest biogenic sources of methane contributing to growing atmospheric CH 4 concentrations (1) and are, in turn, highly sensitive to climate change (2). However, radiative feedbacks from wetland CH 4 emissions were not considered in the Coupled Model Intercomparison Project Phase 5 (CMIP5), and Integrated Assessment Models (IAM) assumed anthropogenic sources to be the only driver responsible for the increase of atmospheric CH 4 burden since the 1750s (3). The role of wetland CH 4 emissions, however, may play an increasingly larger role in future atmospheric growth of methane because of the large stocks of mineral and organic carbon stored under anaerobic conditions in both boreal and tropical regions. Paleoclimatological and contemporary observations of the climate sensitivity of wetland methane emissions suggest the potential for a large feedback (4), but there remains large uncertainty in quantifying the actual range of the response (5, 6).Increasing air temperature is linked to the thawing of permafrost and to increased rates of soil microbial activity (7), which directly lead to greater CH 4 production in soils due to thaw-induced change in surface wetland areas (8). In the tropics, wetland areal extent is also influenced by precipitation, which affects the area of surface inundation, water table depth, and soil moisture that, in turn, promote methanogenesis. Elevated CO 2 concentrations can increase ecosystem water use efficiency and thus soil moisture, and also increase soil carbon substrate availability for microbial activities (9). Tropical wetlands, for which a decline in inundation was observed in recent decades (10), are already exposed to increasing frequencies in extreme climate events, e.g., heat waves, floods, and droughts, and changes in rainfall distribution (11) and variability in methane emissions (12). Meanwhile, northern highlatitude ecosystems are experiencing a more rapid temperature increase than elsewhere globally and with increased rates of soil respiration (13), yet, locally at least, no response in methane emissions (14). Despite the importance of these feedbacks noted by the Intergovernmen...

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Abstract. Evapotranspiration (ET) from the wetland of the Yellow River Delta (YRD) is one of the important components in the water cycle, which represents the water consumption by the plants and evaporation from the water and the non-vegetated surfaces. Reliable estimates of the total evapotranspiration from the wetland is useful information both for understanding the hydrological process and for water management to protect this natural environment. Due to the heterogeneity of the vegetation types and canopy density and of soil water content over the wetland (specifically over the natural reserve areas), it is difficult to estimate the regional evapotranspiration extrapolating measurements or calculations usually done locally for a specific land cover type. Remote sensing can provide observations of land surface conditions with high spatial and temporal resolution and coverage. In this study, a model based on the Energy Balance method was used to calculate daily evapotranspiration (ET) using instantaneous observations of land surface reflectance and temperature from MODIS when the data were available on clouds-free days. A time series analysis algorithm was then applied to generate a time series of daily ET over a year period by filling the gaps in the observation series due to clouds. A detailed vegetation classification map was used to help identifying areas of various wetland vegetation types in the YRD wetland. Such information was also used to improve the parameterizations in the energy balance model to improve the accuracy of ET estimates. This study showed that spatial variation of ET was significant over the same vegetation class at a given time and over different vegetation types in different seasons in the YRD wetland.