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Effect of Vegetation on the Energy Balance and Evapotranspiration in Tallgrass Prairie: A Paired Study Using the Eddy-Covariance Method

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Abstract

We carried out a paired study in tallgrass prairie to evaluate the influence of vegetation on the energy exchange and evapotranspiration. Two eddy-covariance systems were installed over two adjoining sites, one of which was denuded of vegetation, with the adjacent, control site kept undisturbed. Our year-long investigation shows that, for quantifying the ground surface heat flux, the soil heat storage above the soil plates is more important than the sub-surface soil heat flux, both temporally and in magnitude. The incorporation of the soil heat storage, therefore, is indispensable for energy balance closure in areas with short vegetation. At our control site, we observed a critical threshold of 0.17 m3 m−3 in the surface (top 0.3 m) soil water content, whereby the energy partitioning is significantly affected by the presence of the photosynthetically active vegetation when the surface soil water content is higher than this critical threshold. The pattern of energy partitioning approaches that of the treated site when the surface soil water content is lower than this threshold (during drought), because of the suppression of plant physiological activities. This threshold also applies to the surface conductance for water vapour at the control site, where yearly evapotranspiration is 728 ± 3 mm (versus 547 ± 2 mm for the treated site). Thus, the soil water content and presence of active vegetation are the key determinants of energy partitioning and evapotranspiration. Any land-cover changes or vegetation-management practices that alter these two factors may change the energy and water budgets in tallgrass prairie.

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Acknowledgements

We are grateful for the insightful comments from the two anonymous reviewers. This research was funded by the National Science Foundation’s Dynamics of Coupled Natural and Human Systems (CNH) program (DEB-1413900). Xiangmin Sun is a PhD student supported by the Sid Kyle Graduate Merit Assistantships in the Department of Ecosystem Science and Management at Texas A&M University. The authors would like to thank Chris Stansberry and Jay Prater for their excellent management of the research site. We also express our appreciation to Georgios Xenakis for his development of the FREddyPro package and for correspondence with the authors. The authors gratefully acknowledge many useful comments provided by James Heilman. We are deeply appreciative of the technical support provided by James Kathilankal, Jiahong Li, and George G. Burba from LI-COR Biosciences, Inc.; and by Sasha Ivans and Ben Conrad from Campbell Scientific, Inc. Finally, the authors are grateful to the many graduate students who helped with field trips, including Briana Wyatt, Patricia Torquato, Giovanne Serrau, Sumit Sharma, and Cynthia Wright.

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Authors and Affiliations

  1. Department of Ecosystem Science and Management, Texas A&M University, College Station, TX, 77843, USA

    Xiangmin Sun & Bradford Wilcox

  2. Department of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, OK, 74078, USA

    Chris B. Zou & Elaine Stebler

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  1. Xiangmin Sun
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  2. Chris B. Zou
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  3. Bradford Wilcox
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  4. Elaine Stebler
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Corresponding author

Correspondence to Xiangmin Sun.

Appendices

Appendix 1: Analytical Results

The following figures show further results on the prevailing wind direction, soil–water dynamics across the soil profile, the phase shifts between the two components of the ground surface heat flux heat flux G0, average monthly energy partitioning, and seasonal variations in the normalized sensible and latent heat fluxes (Figs. 11, 12, 13, 14, 15).

Fig. 11
figure 11

Wind rose maps for the two sites

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Fig. 12
figure 12

Dynamics of volumetric soil moisture (θ) across the soil profiles at the two sites as measured at four depths—0.05, 0.20, 0.45, and 0.80 m—representing, respectively, the soil–water dynamics for the four depth intervals 0–0.1 m, 0.1–0.3 m, 0.3–0.6 m, and 0.6–1.0 m. Each point represents the daily mean value of θ from two measuring stations within each site. Dashed lines indicate the dates of the herbicide treatment

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Fig. 13
figure 13

Diurnal variation of the ground surface heat flux (G0) and its two components (Ssoil and Gs) during the different seasons for each site: spring (21 March–20 June), summer (21 June–20 September), autumn (21 September–20 December), and winter (21 December–20 March). Each point is a 30-min ensemble mean for its corresponding flux during that entire season with a 95% confidence interval. Negative values represent the upwards diffusion of heat lost from the surface, and positive values represent the downwards absorption through the ground

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Fig. 14
figure 14

Monthly means of all energy and turbulent fluxes (Rn, H, LE, and G0) for the two sites. The bars on each column represent the 95% confidence intervals

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Fig. 15
figure 15

Variations in daily sensible (a) and latent heat (b) fluxes normalized by the available energy and Bowen ratios (c). Each point in the normalized value and Bowen ratio represents the daytime average when the global radiation is higher than 20 W m−2. All three series were smoothed by a locally weighted regression with a span of 0.1. The two vertical dashed lines represent the dates of herbicide application

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Appendix 2: Code Availability

The sample R code for extracting the planetary boundary-layer height based on the geographical location from the North American Regional Reanalysis data is available as open source from the first author’s GitHub webpage at https://github.com/sunxm19/Planetary_boundary_height_for_FFP.

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Sun, X., Zou, C.B., Wilcox, B. et al. Effect of Vegetation on the Energy Balance and Evapotranspiration in Tallgrass Prairie: A Paired Study Using the Eddy-Covariance Method. Boundary-Layer Meteorol 170, 127–160 (2019). https://doi.org/10.1007/s10546-018-0388-9

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