Ecosystem-scale water-use efficiency (WUE), defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET), is an important indicator of coupled carbon-water cycles. Relationships between WUE and environmental factors have been widely investigated, but the variations in WUE in response to biotic factors remain little understood. Here, we argue that phenology plays an important role in the regulation of WUE by analyzing seasonal WUE responses to variability of photosynthetic phenological factors in terrestrial ecosystems of the Northern Hemisphere using MODIS satellite observations during 2000–2014. Our results show that WUE, during spring and autumn is widely and significantly correlated to the start (SOS) and end (EOS) of growing season, respectively, after controlling for environmental factors (including temperature, precipitation, radiation and atmospheric carbon dioxide concentration). The main patterns of WUE response to phenology suggest that an increase in spring (or autumn) WUE with an earlier SOS (or later EOS) are mainly because the increase in GPP is relatively large in magnitude compared to that of ET, or due to an increase in GPP accompanied by a decrease in ET, resulting from an advanced SOS (or a delayed EOS). Our results and conclusions are helpful to complement our knowledge of the biological regulatory mechanisms underlying coupled carbon-water cycles.

Timely and accurate detection of microclimates is extremely valuable for monitoring and stimulating exchanges of mass and energy in forest ecosystems under climate change. Recently, the rapid growth of wireless sensor networks (WSNs) has provided a new approach for detecting microclimates in a complex environment at multiple temporal and spatial scales. However, applications of wireless sensors in forest microclimate monitoring have rarely been studied, and the corresponding observation accuracy, error sources and correction methods are not well understood. In this study, through field experiments in two typical subtropical forest ecosystems in Zhejiang Province, China, the accuracy of the temperature and humidity observed by the wireless sensors was evaluated against standard meteorological data. Furthermore, the observation error sources were analyzed and corresponding correction models were established. The results showed that the wireless sensor-based temperature and humidity values performed well within the total observation accuracy. However, the observation errors varied with season, daily periodicity and weather conditions. For temperature, the wireless sensor observations were overestimated during the daytime while they were underestimated during the nighttime. For humidity, the data observed by the wireless sensors generally appeared as overestimates. Adopting humidity as the corrected factor, correction models were established and effectively improved the accuracy of the microclimatic data observed by the wireless sensors. Notably, our error analysis demonstrated that the observation errors may be associated with the shell material of the wireless sensor, suggesting that shading measures for the wireless sensors should be considered for outdoor work.