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Using measured soil water contents to estimate evapotranspiration and root water uptake profiles - a comparative study

机译:利用测得的土壤含水量估算蒸散量和根系水分吸收曲线-对比研究

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Understanding the role of plants in soil water relations, and thus ecosystem functioning, requires information about root water uptake. We evaluated four different complex water balance methods to estimate sink term patterns and evapotranspiration directly from soil moisture measurements. We tested four methods. The first two take the difference between two measurement intervals as evapotranspiration, thus neglecting vertical flow. The third uses regression on the soil water content time series and differences between day and night to account for vertical flow. The fourth accounts for vertical flow using a numerical model and iteratively solves for the sink term. None of these methods requires any a priori information of root distribution parameters or evapotranspiration, which is an advantage compared to common root water uptake models. To test the methods, a synthetic experiment with numerical simulations for a grassland ecosystem was conducted. Additionally, the time series were perturbed to simulate common sensor errors, like those due to measurement precision and inaccurate sensor calibration. We tested each method for a range of measurement frequencies and applied performance criteria to evaluate the suitability of each method. In general, we show that methods accounting for vertical flow predict evapotranspiration and the sink term distribution more accurately than the simpler approaches. Under consideration of possible measurement uncertainties, the method based on regression and differentiating between day and night cycles leads to the best and most robust estimation of sink term patterns. It is thus an alternative to more complex inverse numerical methods. This study demonstrates that highly resolved (temporally and spatially) soil water content measurements may be used to estimate the sink term profiles when the appropriate approach is used.
机译:要了解植物在土壤水关系中的作用,以及因此在生态系统中的作用,就需要有关根系水分吸收的信息。我们评估了四种不同的复杂水平衡方法,以直接从土壤水分测量值中估算汇期模式和蒸散量。我们测试了四种方法。前两个将两个测量间隔之间的差值作为蒸散量,因此忽略了垂直流。第三类使用土壤水分时间序列的回归和昼夜差异来解释垂直流。第四个使用数值模型说明了垂直流,并迭代求解了汇项。这些方法都不需要任何有关根系分配参数或蒸散量的先验信息,与普通的根系吸水模型相比,这是一个优势。为了测试这些方法,对草地生态系统进行了数值模拟的综合实验。此外,时间序列受到干扰以模拟常见的传感器误差,例如由于测量精度和传感器校准不准确而引起的误差。我们测试了每种方法的测量频率范围,并应用了性能标准来评估每种方法的适用性。一般而言,我们表明,与简单方法相比,解释垂直流的方法可以更准确地预测蒸散量和汇项分布。考虑到可能的测量不确定性,基于回归并区分昼夜周期的方法导致对汇项模式的最佳和最稳健的估计。因此,它是更复杂的逆数值方法的替代方法。这项研究表明,当使用适当的方法时,高度解析的(时间和空间上的)土壤含水量测量值可用于估算汇项的轮廓。

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