Novel Keeling-plot-based methods to estimate the isotopic composition of ambient water vapor

Yuan, Yongchao; Du, Taisheng; Wang, Honglang; Wang, Lixin

doi:10.5194/hess-24-4491-2020

Cited by 3 publications

(2 citation statements)

References 56 publications

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“…When estimating vegetation and soil isotope compositions, we only selected five representative vegetation and soil samples at two sites. There is also significant uncertainty when using steady-state isotope assumptions and Keeling plot curve methods to estimate vegetation and atmospheric isotope compositions [40,41]. Similar errors also existed in the ERA-5-based SWC and LAI observations [42].…”

Section: Uncertainty In the Model Comparison Analysismentioning

confidence: 98%

“…Finally, the hydrogen and oxygen isotope compositions of the vegetation, soil, and air samples were analyzed using a hydrogen and oxygen isotope analyzer (Licor, TIWA-912). Three isotope end-members included the isotopic composition of vegetation, soil, and atmosphere samples, which were estimated using stable isotope as-sumptions [40], C-G models [13], and Keeling plot curves [41], respectively. The vegetation transpiration fraction (T/ET) was estimated using isotope mass conservation theory.…”

Section: Stable Isotope Observationmentioning

confidence: 99%

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Evaluation the Performance of Three Types of Two-Source Evapotranspiration Models in Urban Woodland Areas

et al. 2023

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The determination of the evapotranspiration (ET) and its components in urban woodlands is crucial to mitigate the urban heat island effect and improve sustainable urban development. However, accurately estimating ET in urban areas is more difficult and challenging due to the heterogeneity of the underlying surface and the impact of human activities. In this study, we compared the performance of three types of classic two-source ET models on urban woodlands in Shenzhen, China. The three ET models include a pure physical and process-based ET model (Shuttleworth–Wallace model), a semi-empirical and physical process-based ET model (FAO dual-Kc model), and a purely statistical and process-based ET model (deep neural network). The performance of the three models was validated using an eddy correlation and stable hydrogen and oxygen isotope observations. The verification results suggested that the Shuttleworth–Wallace model achieved the best performance in the ET simulation at main urban area site (coefficient of determination (R2) of 0.75). The FAO-56 dual Kc model performed best in the ET simulation at the suburb area site (R2 of 0.77). The deep neural network could better capture the nonlinear relationship between ET and various environmental variables and achieved the best simulation performance in both of the main urban and suburb sites (R2 of 0.73 for the main urban and suburb sites, respectively). A correlation analysis showed that the simulation of urban ET is most sensitive to temperature and least sensitive to wind speed. This study further analyzed the causes for the varying performance of the three classic ET models from the model mechanism. The results of the study are of great significance for urban temperature cooling and sustainable urban development.

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Section: Uncertainty In the Model Comparison Analysismentioning

confidence: 98%

Section: Stable Isotope Observationmentioning

confidence: 99%

Evaluation the Performance of Three Types of Two-Source Evapotranspiration Models in Urban Woodland Areas

et al. 2023

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A modified isotope-based method for potential high-frequency evapotranspiration partitioning

Yuan

Wang

et al. 2022

Advances in Water Resources

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Three Bayesian Tracer Models: Which Is Better for Determining Sources of Root Water Uptake Based on Stable Isotopes under Various Soil Water Conditions?

Liu

et al. 2023

Agronomy

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Stable hydrogen and oxygen isotopes provide a powerful technique for quantifying the proportion of root water uptake (RWU) from different potential water sources. Although many models coupled with stable isotopes have been developed to estimate plant water source apportionment, inter-comparisons of different methods are still limited, especially their performance under different soil water content (SWC) conditions. In this study, three Bayesian tracer mixing models, which included MixSIAR, MixSIR and SIAR, were tested to evaluate their performances in determining the RWU of winter wheat under various SWC conditions (normal, dry and wet) in the North China Plain (NCP). The proportions of RWU in different soil layers showed significant differences (p < 0.05) among the three Bayesian models, for example, the proportion of 0–20 cm soil layer calculated by MixSIR, MixSIAR and SIAR was 69.7%, 50.1% and 48.3% for the third sampling under the dry condition (p < 0.05), respectively. Furthermore, the average proportion of the 0–20 cm layer under the dry condition was lower than that under normal and wet conditions, being 45.7%, 58.3% and 59.5%, respectively. No significant difference (p > 0.05) was found in the main RWU depth (i.e., 0–20 cm) among the three models, except for individual sampling periods. The performance of three models in determining plant water source allocation varied with SWC conditions: the performance indicators such as coefficient of determination (R2) and Nash-Sutcliffe efficiency coefficient (NS) in MixSIAR were higher than that in MixSIR and SIAR, showing that MixSIAR performed well under normal and wet conditions. The rank of performance under the dry condition was MixSIR, MixSIAR, and then SIAR. Overall, MixSIAR performed relatively better than other models in predicting RWU under the three different soil moisture conditions.

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Novel Keeling-plot-based methods to estimate the isotopic composition of ambient water vapor

Cited by 3 publications

References 56 publications

Evaluation the Performance of Three Types of Two-Source Evapotranspiration Models in Urban Woodland Areas

Evaluation the Performance of Three Types of Two-Source Evapotranspiration Models in Urban Woodland Areas

A modified isotope-based method for potential high-frequency evapotranspiration partitioning

Three Bayesian Tracer Models: Which Is Better for Determining Sources of Root Water Uptake Based on Stable Isotopes under Various Soil Water Conditions?

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