Wetlands are important modulators of atmospheric greenhouse gas (GHGs) concentrations. However, little is known about the magnitudes and spatiotemporal patterns of GHGs fluxes in wetlands on the Qinghai-Tibetan Plateau (QTP), the world's largest and highest plateau. In this study, we measured soil temperature and the fluxes of carbon dioxide (CO 2) and methane (CH 4) in an alpine wetland on the QTP from April 2017 to April 2019 by the static chamber method, and from January 2017 to December 2017 by the eddy covariance (EC) method. The CO 2 and CH 4 emission measurements from both methods showed different relationships to soil temperature at different timescales (annual and seasonal). Based on such relationship patterns and soil temperature data (1960-2017), we extrapolated the CO 2 and CH 4 emissions of study site for the past 57 years: the mean CO 2 emission rate was 1096.59 mg C m −2 h −1 on different measurement methods and timescales, with the range of the mean emission rate from 421.17 to 1754.99 mg C m −2 h −1 , while the mean CH 4 emission rate was 32.99 mg C m −2 h −1 , with the ranges of the mean emission rate from 16.95 to 46.25 mg C m −2 h −1. The estimated regional CO 2 and CH 4 emissions from permanent wetlands on the QTP were 94.29 and 2.37 Tg C year −1 , respectively. These results indicate that uncertainties caused by measuring method and timescale should be fully considered when extrapolating wetland GHGs fluxes from local sites to the regional level. Moreover, the results of global warming potential showed that CO 2 dominates the GHG balance of wetlands on the QTP.

Data availability for in situ spatial variability assessment of hydraulic parameters is always limited in the vadose zone. In this work, laboratory and in situ experimental methods of parameter estimation were compared to investigate the best estimation method for heterogeneous soil. The Marquardt–Levenberg and non-linear least-squares optimisation algorithms were used for parameter estimation. The simulation error was minimised by selecting sensitive parameters during the numerical solution. The shape factor n was found to be the most sensitive parameter, followed by water content θs, saturated hydraulic conductivity (SHC) and the inverse of the air entry α. Compared with the in situ cumulative infiltration and simultaneous methods, the outflow method resulted in the best fit by minimising the error. During the comparison of outflow and cumulative infiltration methods, only θs showed a significant difference (p = 0.00). On the other hand, SHC showed a non-significant difference (p = 0.439) when the outflow and simultaneous methods were compared. During model predictions, the SHC measured by the simultaneous method showed reasonable estimates for surface horizon and weak correlations (0.79 and 0.77) with deep soil water content, which could be improved by adding more hydraulic parameters. The cumulative infiltration numerical solution resulted in the most reliable estimates of hydraulic parameters for in situ heterogeneous soil.