55 research outputs found

    Underestimated ecosystem carbon turnover time and sequestration under the steady state assumption: a perspective from long‐term data assimilation

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    It is critical to accurately estimate carbon (C) turnover time as it dominates the uncertainty in ecosystem C sinks and their response to future climate change. In the absence of direct observations of ecosystem C losses, C turnover times are commonly estimated under the steady state assumption (SSA), which has been applied across a large range of temporal and spatial scales including many at which the validity of the assumption is likely to be violated. However, the errors associated with improperly applying SSA to estimate C turnover time and its covariance with climate as well as ecosystem C sequestrations have yet to be fully quantified. Here, we developed a novel model-data fusion framework and systematically analyzed the SSA-induced biases using time-series data collected from 10 permanent forest plots in the eastern China monsoon region. The results showed that (a) the SSA significantly underestimated mean turnover times (MTTs) by 29%, thereby leading to a 4.83-fold underestimation of the net ecosystem productivity (NEP) in these forest ecosystems, a major C sink globally; (b) the SSA-induced bias in MTT and NEP correlates negatively with forest age, which provides a significant caveat for applying the SSA to young-aged ecosystems; and (c) the sensitivity of MTT to temperature and precipitation was 22% and 42% lower, respectively, under the SSA. Thus, under the expected climate change, spatiotemporal changes in MTT are likely to be underestimated, thereby resulting in large errors in the variability of predicted global NEP. With the development of observation technology and the accumulation of spatiotemporal data, we suggest estimating MTTs at the disequilibrium state via long-term data assimilation, thereby effectively reducing the uncertainty in ecosystem C sequestration estimations and providing a better understanding of regional or global C cycle dynamics and C-climate feedback

    Estimating the Legacy Effect of Post-Cutting Shelterbelt on Crop Yield Using Google Earth and Sentinel-2 Data

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    Shelterbelts (or windbreaks) can effectively improve the microclimate and soil conditions of adjacent farmland and thus increase crop yield. However, the individual contribution of these two factors to yield changes is still unclear since the short-term effect from the microclimate and the accumulated effect from the soil jointly affect crop yield. The latter (soil effect) is supposed to remain after shelterbelt-cutting, thus inducing a post-cutting legacy effect on yield, which can be used to decompose the shelterbelt-induced yield increase. Here, we develop an innovative framework to investigate the legacy effect of post-cutting shelterbelt on corn yield by combining Google Earth and Sentinel-2 data in Northeastern China. Using this framework, for the first time, we decompose the shelterbelt-induced yield increase effect into microclimate and soil effects by comparing the yield profiles before and after shelterbelt-cutting. We find that on average, the intensity of the legacy effect, namely the crop yield increment of post-cutting shelterbelts, is 0.98 +/- 0.03%. The legacy effect varies depending on the shelterbelt-farmland relative location and shelterbelt density. The leeward side of the shelterbelt-adjacent farmland has a more remarkable legacy effect compared to the windward side. Shelterbelts with medium-high density have the largest legacy effect (1.94 +/- 0.05%). Overall, the legacy effect accounts for 47% of the yield increment of the shelterbelt before cutting, implying that the soil effect is almost equally important for increasing crop yield compared to the microclimate effect. Our findings deepen the understanding of the mechanism of shelterbelt-induced yield increase effects and can help to guide shelterbelt management