Improved data gap-filling schemes for estimation of net ecosystem exchange in typical East-Asian croplands

Abstract

The estimation of carbon exchange between ecosystems and the atmosphere suffers unavoidable data gaps in eddy-covariance technique, especially for short-living and fast-growing croplands. In this study we developed a modified gap-filling scheme introducing a leaf area index factor as the vegetation status information based on the conventional light response function for two East-Asian cropland sites (rice and potatoes). This scheme's performance is comparable to the conventional time window scheme, but has the advantage when the gaps are large compared to the total length of the time series. To investigate how the time binning approach performs for fast-growing croplands, we tested different widths of the time window, showing that a four-day window for the potato field and an eight-day time window for the rice field perform the best. The insufficiency of the conventional temperature binning approach was explained as well as the influence of vapor pressure deficit. We found that vapor pressure deficit plays a minor role in both the potato and the rice fields under Asian monsoon weather conditions with the exception of the early pre-monsoon growing stage of the potatoes. Consequently, we recommend using the conventional time-window scheme together with our new leaf-light response function to fill data gaps of net ecosystem exchange in fast-growing croplands.

Parameterization of Evapotranspiration Estimation for Two Typical East Asian Crops

“…It was reported that 1/r s decreases linearly with log(VPD) for plant species and stomatal sensitivity is proportional to the magnitude of 1/r s at low VPD (≤10 hPa) [49], which was later demonstrated to be consistent with the linear model presented by [50]. High temperature as well as high VPD in the afternoon in dry pre-monsoon season leads to a high evapotranspiration rate and then to a stomatal closure for potato plants in the research region [28]. This regulation could result in a deviation of r s from the estimation by Equations (6) and (7), and consequently the poor efficiency of both the PM-KP approach and the PM-FAO approach in the case of the dry air.…”

“…It was reported that the observed VPD is below the plant physiological threshold (10 hPa) during most of the time of the growing season of rice owing to the permanently flooded terraces with the contribution of intensive precipitation in summer monsoon in the research region [28]. However, the model performance of the PM-KP approach was weak for dry air conditions in the potato field, possibly because of the regulation of leaf water potential by stomata.…”

“…Finally, the 30-min dataset, excluding low quality data, irrelevant records, and outliers by a multiple-step filter [24], was used as the high-quality database for subsequent parameterization. For further information about the field campaign, please refer to [27,28].…”

“…Seventy-five percent (75%) of the annual precipitation fell in the crop growing season from June to September. Large gaps of continuous days were found in eddy covariance measurement during these rain events because of the poor instrument status [28].…”

On the calculation of daytime CO2 fluxes measured by automated closed transparent chambers

“…Considering the small change rate of chamber air temperature, the deviation in the VPD estimation is slight. As a consequence, the water vapor condensed on the inner wall of the chamber dome gradually, which reduced the solar radiation in the chamber and as a consequence the photosynthesis of the vegetation (Zhao & Lüers, 2016), and thus the net exchange of CO 2 turned from negative (towards the surface) to positive (away from the surface) after t s .…”