RESUMEN

Wetlands are important carbon sinks. However, the carbon sequestration potential of flooded wetlands may be weakened owing to water regime changes induced by anthropogenic disturbances. Using the eddy covariance technique, this study quantified the effects of the water level and vegetation types on the net ecosystem CO2 exchange (NEE), gross primary production (GPP), and ecosystem respiration (Reco) from a reed marsh (Miscanthus sacchariflorus) and a sedge meadow (Carex spp.) in the Dongting Lake floodplain from 2014 to 2016. Our results indicated that the sedge meadow (-89.49 to -186.47 g C m-2 y-1) and reed marsh (-246.12 to -513.94 g C m-2 y-1) were carbon sinks on the interannual timescale. However, the sedge meadow changed from a carbon sink to a carbon source during the flooding season. The effect of flooding on the carbon sink function in the reed marsh was dependent on the water level. The carbon sink function of the reed marsh was enhanced by moderate flooding (water level under 30.5 m in Chenglingji) owing to the inhibition of Reco, but was weakened by extremely high-water levels (over 33 m in Chenglingji) during the flooding season. Seasonal variations in NEE, GPP, and Reco were closely related to photosynthetic photon flux density, soil water content, water level, soil temperature, and air temperature. We can conclude that the increase in reed area combined with the decrease in flooding days in the sedge meadow can potentially enhance the carbon sink function of the Dongting Lake floodplain.

RESUMEN

Carbon dioxide (CO2) emissions from aquatic ecosystems are important components of the global carbon cycle, yet the CO2 emissions from coastal reservoirs, especially in developing countries where urbanization and rapid land use change occur, are still poorly understood. In this study, the spatiotemporal variations in CO2 concentrations and fluxes were investigated in Wenwusha Reservoir located in the southeast coast of China. Overall, the mean CO2 concentration and flux across the whole reservoir were 41.85 ± 2.03 µmol/L and 2.87 ± 0.29 mmol/m2/h, respectively, and the reservoir was a consistent net CO2 source over the entire year. The land use types and urbanization levels in the reservoir catchment significantly affected the input of exogenous carbon to water. The mean CO2 flux was much higher from waters adjacent to the urban land (5.05 ± 0.87 mmol/m2/hr) than other land use types. Sites with larger input of exogenous substance via sewage discharge and upstream runoff were often the hotspots of CO2 emission in the reservoir. Our results suggested that urbanization process, agricultural activities, and large input of exogenous carbon could result in large spatial heterogeneity of CO2 emissions and alter the CO2 biogeochemical cycling in coastal reservoirs. Further studies should characterize the diurnal variations, microbial mechanisms, and impact of meteorological conditions on reservoir CO2 emissions to expand our understanding of the carbon cycle in aquatic ecosystems.

Asunto(s)

Dióxido de Carbono , Urbanización , Dióxido de Carbono/análisis , China , Ecosistema , Metano/análisis

RESUMEN

The eddy covariance (EC) technique is used to measure the net ecosystem exchange (NEE) of CO2 between ecosystems and the atmosphere, offering a unique opportunity to study ecosystem responses to climate change. NEE is the difference between the total CO2 release due to all respiration processes (RECO), and the gross carbon uptake by photosynthesis (GPP). These two gross CO2 fluxes are derived from EC measurements by applying partitioning methods that rely on physiologically based functional relationships with a limited number of environmental drivers. However, the partitioning methods applied in the global FLUXNET network of EC observations do not account for the multiple co-acting factors that modulate GPP and RECO flux dynamics. To overcome this limitation, we developed a hybrid data-driven approach based on combined neural networks (NNC-part ). NNC-part incorporates process knowledge by introducing a photosynthetic response based on the light-use efficiency (LUE) concept, and uses a comprehensive dataset of soil and micrometeorological variables as fluxes drivers. We applied the method to 36 sites from the FLUXNET2015 dataset and found a high consistency in the results with those derived from other standard partitioning methods for both GPP (R2  > .94) and RECO (R2  > .8). High consistency was also found for (a) the diurnal and seasonal patterns of fluxes and (b) the ecosystem functional responses. NNC-part performed more realistic than the traditional methods for predicting additional patterns of gross CO2 fluxes, such as: (a) the GPP response to VPD, (b) direct effects of air temperature on GPP dynamics, (c) hysteresis in the diel cycle of gross CO2 fluxes, (d) the sensitivity of LUE to the diffuse to direct radiation ratio, and (e) the post rain respiration pulse after a long dry period. In conclusion, NNC-part is a valid data-driven approach to provide GPP and RECO estimates and complementary to the existing partitioning methods.

Asunto(s)

Dióxido de Carbono , Ecosistema , Ciclo del Carbono , Redes Neurales de la Computación , Fotosíntesis , Respiración , Estaciones del Año

RESUMEN

In high mountains, the effects of climate change are manifesting most rapidly. This is especially critical for the high-altitude carbon cycle, for which new feedbacks could be triggered. However, mountain carbon dynamics is only partially known. In particular, models of the processes driving carbon fluxes in high-altitude grasslands and Alpine tundra need to be improved. Here, we propose a comparison of three empirical approaches using systematic statistical analysis, to identify the environmental variables controlling CO2 fluxes. The methods were applied to a complete dataset of simultaneous in situ measurements of the net CO2 exchange, ecosystem respiration and basic environmental variables in three sampling sites in the same catchment. Large year-to-year variations in the Gross Primary Production (GPP) and Ecosystem Respiration (ER) dependences on solar irradiance and temperature were observed. We thus implemented a multi regression model in which additional variables were introduced as perturbations of the standard exponential and rectangular hyperbolic functions for ER and GPP, respectively. A comparison of this model with other common modelling strategies showed the benefits of this approach, resulting in large explained variances (83% to 94%). The optimum ensemble of variables explaining the inter- and intra-annual flux variability included solar irradiance, soil moisture and day of the year for GPP, and air temperature, soil moisture, air pressure and day of the year for ER, in agreement with other studies. The modelling approach discussed here provides a basis for selecting drivers of carbon fluxes and understanding their role in high-altitude Alpine ecosystems, also allowing for future short-range assessments of local trends.

RESUMEN

The hysteresis of the seasonal relationships between vegetation indices (VIs) and gross ecosystem production (GEP) results in differences between these relationships during vegetative and reproductive phases of plant development cycle and may limit their applicability for estimation of croplands productivity over the entire season. To mitigate this problem and to increase the accuracy of remote sensing-based models for GEP estimation we developed a simple empirical model where greenness-related VIs are multiplied by the leaf area index (LAI). The product of this multiplication has the same seasonality as GEP, and specifically for vegetative periods of winter crops, it allowed the accuracy of GEP estimations to increase and resulted in a significant reduction of the hysteresis of VIs vs. GEP. Our objective was to test the multiyear relationships between VIs and daily GEP in order to develop more general models maintaining reliable performance when applied to years characterized by different climatic conditions. The general model parametrized with NDVI and LAI product allowed to estimate daily GEP of winter and spring crops with an error smaller than 14%, and the rate of GEP over- (for spring barley) or underestimation (for winter crops and potato) was smaller than 25%. The proposed approach may increase the accuracy of crop productivity estimation when greenness VIs are saturating early in the growing season.

RESUMEN

Greenhouse gas emission from aquatic ecosystem will affect climate change on the regional and global scale. So large river interception project which affects the carbon cycle model and greenhouse gas emission from aquatic ecosystem has provoked more and more attentions in recent years. In order to understand and evaluate the effects of typical hydropower project construction on the aquatic ecosystem and carbon cycle, the Geheyan Reservoir, a typical river interception project, was selected as a typical case in this paper for measuring carbon dioxide fluxes from interface between water and atmosphere from March, 2015 to February, 2016. The integration of the online gas analyzer and floating box was used to obtain carbon dioxide fluxes from interface between water and atmosphere. Data was obtained over the before dam, upstream, tributary, drawdown area and bay area respectively for understanding the spatial Heterogeneity of carbon dioxide fluxes. Data analysis showed that yearly mean fluxes of carbon dioxide from the Geheyan Reservoir was (55.6918±66.3329) mg·(m2·h)-1 during measurement, which indicated that the temporal distribution was higher in winter and lower in other seasons and the spatial variation was higher in typical bay and lower before dam as well as drawdown zone over the reservoir. Moreover, the seasonal variation of carbon dioxide fluxes from the interface between water and atmosphere was very stable at Yuxiakou measurement points as a typical background area of the reservoir, which was unexpectedly higher than those before dam and drawdown zone in the most months during measurement. The results from data analysis also indicated that the spatial and temporal variation of carbon dioxide fluxes from the Geheyan Reservoir was affected by water quality parameters such as dissolved oxygen, chlorophyll, pH, water temperature and conductivity and carbon in water, but the relationship between carbon dioxide fluxes and other parameter was very different within each season and impound period. So the results above will provide more supports for understanding the import pathway and transfer of aquatic carbon cycle caused by large river interception project in China.