Groundwater Regulates Interannual Variations in Evapotranspiration in a Riparian Semiarid Ecosystem

Abstract

Drylands are an important component of Earth's carbon, water, and energy budgets. Changes in hydroclimatic conditions can significantly alter land surface fluxes in these ecosystems. In regions with groundwater‐river water exchange, land surface fluxes are strongly modulated by groundwater table fluctuations in response to river stage variations; however, interactions between fluxes and groundwater remain poorly understood in drylands. Here, we use eddy covariance data collected in paired upland and riparian semiarid ecosystems (AmeriFlux sites US‐Hn1 and US‐Hn2) during 4 years with contrasting river flow conditions to examine interactions among river stage, water table dynamics, meteorological drivers, and ecosystem fluxes. Elevated groundwater availability resulting from groundwater‐surface water exchange at the riparian site enhanced the latent heat flux (LE, the energy form of evapotranspiration), which was sustained longer in the growing season. Interannual variation of dry season LE was small at the upland site, while at the riparian site contrasting river flow conditions resulted in large interannual variability in LE, with June LE of high‐flow years being 2.3 times that of low‐flow years. The 4‐year mean summer LE at the riparian site was 2.1 times larger than that at the upland site. Carbon uptake (NEP) and LE were well correlated at the upland site at the monthly and annual timescales, but at the riparian site enhanced LE due to shallow water table depth did not correspond to enhanced NEP. Our results demonstrate that enhanced groundwater availability modulated by river stage is a critical driver of land surface fluxes in semiarid riparian ecosystems.

Technical note: Uncertainties in eddy covariance CO<sub>2</sub> fluxes in a semiarid sagebrush ecosystem caused by gap-filling approaches

“…The eddy covariance (EC) technique has been widely applied for monitoring energy and water fluxes as well as net ecosystem exchanges (NEEs) of carbon dioxide and other trace gases between land and the atmosphere (Baldocchi, 2003;Oncley et al, 2007;Stoy et al, 2013). However, due to multiple factors including power outages, instrument malfunctions and maintenance, and data quality checks, there exist gaps with approximately 20 %-60 % of half-hourly data points annually at many long-term EC sites (Dragoni et al, 2007;Falge et al, 2001;Ma et al, 2007;Missik et al, 2019Missik et al, , 2021Moffat et al, 2007;Pastorello et al, 2020;Soloway et al, 2017;Wutzler et al, 2018). An average gap fraction of 30 % in an annual dataset leads to an uncertainty of ±25 g C m −2 yr −1 for the annual NEE at forest sites (Moffat et al, 2007), while some EC sites report much greater uncertainties (Soloway et al, 2017).…”

“…However, due to multiple factors including power outages, instrument malfunctions and maintenance, and data quality checks, there exist gaps with approximately 20 %-60 % of half-hourly data points annually at many long-term EC sites (Dragoni et al, 2007;Falge et al, 2001;Ma et al, 2007;Missik et al, 2019Missik et al, , 2021Moffat et al, 2007;Pastorello et al, 2020;Soloway et al, 2017;Wutzler et al, 2018). An average gap fraction of 30 % in an annual dataset leads to an uncertainty of ±25 g C m −2 yr −1 for the annual NEE at forest sites (Moffat et al, 2007), while some EC sites report much greater uncertainties (Soloway et al, 2017). Therefore, gap-filling usually accounts for one large source of uncertainties in the annual NEE (Soloway et al, 2017), together with other sources of uncertainties such as measurement errors and bias related to non-closure of the surface energy balance (Gao et al, 2019;Wilson et al, 2002).…”

“…Robust NEE gap-filling approaches are critical for quantifying the annual and interannual variability of carbon budgets (Falge et al, 2001;Irvin et al, 2021;Moffat et al, 2007;Pastorello et al, 2020;Richardson and Hollinger, 2007;Soloway et al, 2017;Wutzler et al, 2018). Previous studies have developed and evaluated a number of NEE gapfilling approaches including non-linear regressions (NLRs), look-up tables (e.g., marginal distribution sampling, MDS), machine learning (ML) algorithms (e.g., artificial neural networks), and process-based models (Falge et al, 2001;Huang and Hsieh, 2020;Moffat et al, 2007;Reichstein et al, 2005;Wutzler et al, 2018).…”

“…Robust NEE gap-filling approaches are critical for quantifying the annual and interannual variability of carbon budgets (Falge et al, 2001;Irvin et al, 2021;Moffat et al, 2007;Pastorello et al, 2020;Richardson and Hollinger, 2007;Soloway et al, 2017;Wutzler et al, 2018). Previous studies have developed and evaluated a number of NEE gapfilling approaches including non-linear regressions (NLRs), look-up tables (e.g., marginal distribution sampling, MDS), machine learning (ML) algorithms (e.g., artificial neural networks), and process-based models (Falge et al, 2001;Huang and Hsieh, 2020;Moffat et al, 2007;Reichstein et al, 2005;Wutzler et al, 2018). NLR fills NEE gaps based on regression analyses between NEE and meteorological variables such as temperature (e.g., air or soil temperature) and light (e.g., photosynthetically active radiation), whereas MDS is based on look-up tables for similar meteorological conditions (i.e., global radiation, air temperature, and vapor pressure deficit) (Falge et al, 2001;Moffat et al, 2007;Reichstein et al, 2005).…”

“…Previous studies have developed and evaluated a number of NEE gapfilling approaches including non-linear regressions (NLRs), look-up tables (e.g., marginal distribution sampling, MDS), machine learning (ML) algorithms (e.g., artificial neural networks), and process-based models (Falge et al, 2001;Huang and Hsieh, 2020;Moffat et al, 2007;Reichstein et al, 2005;Wutzler et al, 2018). NLR fills NEE gaps based on regression analyses between NEE and meteorological variables such as temperature (e.g., air or soil temperature) and light (e.g., photosynthetically active radiation), whereas MDS is based on look-up tables for similar meteorological conditions (i.e., global radiation, air temperature, and vapor pressure deficit) (Falge et al, 2001;Moffat et al, 2007;Reichstein et al, 2005). By virtue of an easy-to-use R package (Wutzler et al, 2018), MDS has become the standard method for NEE gap-filling (e.g., Pastorello et al, 2020), although it cannot effectively fill the gaps of longer than 12 d (Moffat et al, 2007).…”