Spatial-temporal variability of soil moisture and its estimation across scales
Spatial-temporal variability of soil moisture and its estimation across scales
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DOI：
10.1029/2009wr008016
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发表时间：
2010-02-18
影响因子：
5.4
通讯作者：
Morbidelli, R.
中科院分区：
文献类型：
--
作者：
Brocca, L.;Melone, F.;Morbidelli, R.
土壤水分是研究水文现象和土壤-大气相互作用的重要物理量。由于其高度的空间和时间变异性，土壤水分监测方案进行了研究，无论是土壤水分遥感反演，并在考虑使用的土壤水分数据的径流模拟。为此，通过使用便携式时域反射仪，在一年内在位于意大利中部Vallaccia流域内的七个区域进行了35天的连续测量，面积为60 km（2）。每个采样日，在每个田地收集土壤水分测量值，并在2000 m（2）的常规网格上进行。监测方案的优化，在现场和集水区尺度上准确的平均土壤水分估计的目的，解决了统计和时间稳定性。在实地规模，所需的样本（NRS），以估计在2%的准确度内，遥感土壤水分的验证所需的实地平均土壤水分，范围在4和15之间的几乎干燥的条件（最坏的情况）;在集水区规模，这个数字增加到近40，它是指几乎潮湿的条件。另一方面，估计平均土壤水分的时间模式，有用的径流模拟，NRS被发现是较低的。事实上，在流域尺度上，只有10个测量收集在最“代表性”的领域，以前确定的时间稳定性分析，可以重现流域平均土壤水分的决定系数，R-2，高于0.96和均方根误差，RMSE，等于2.38%。对于“非代表性”领域的RMSE方面的准确性下降，但类似的R-2 coefficients被发现，这种洞察力可以被利用的采样在一个通用的领域时，它是足够的，知道一个指数的土壤水分的时间模式被纳入概念性的土壤径流模型。研究结果可为土壤水分监测网络设计提供参考，并可为流域尺度土壤水分时空分布提供可靠的依据。
The soil moisture is a quantity of paramount importance in the study of hydrologic phenomena and soil-atmosphere interaction. Because of its high spatial and temporal variability, the soil moisture monitoring scheme was investigated here both for soil moisture retrieval by remote sensing and in view of the use of soil moisture data in rainfall-runoff modeling. To this end, by using a portable Time Domain Reflectometer, a sequence of 35 measurement days were carried out within a single year in seven fields located inside the Vallaccia catchment, central Italy, with area of 60 km(2). Every sampling day, soil moisture measurements were collected at each field over a regular grid with an extension of 2000 m(2). The optimization of the monitoring scheme, with the aim of an accurate mean soil moisture estimation at the field and catchment scale, was addressed by the statistical and the temporal stability. At the field scale, the number of required samples (NRS) to estimate the field-mean soil moisture within an accuracy of 2%, necessary for the validation of remotely sensed soil moisture, ranged between 4 and 15 for almost dry conditions (the worst case); at the catchment scale, this number increased to nearly 40 and it refers to almost wet conditions. On the other hand, to estimate the mean soil moisture temporal pattern, useful for rainfall-runoff modeling, the NRS was found to be lower. In fact, at the catchment scale only 10 measurements collected in the most "representative" field, previously determined through the temporal stability analysis, can reproduce the catchment-mean soil moisture with a determination coefficient, R-2, higher than 0.96 and a root-mean-square error, RMSE, equal to 2.38%. For the "nonrepresentative" fields the accuracy in terms of RMSE decreased, but similar R-2 coefficients were found. This insight can be exploited for the sampling in a generic field when it is sufficient to know an index of soil moisture temporal pattern to be incorporated in conceptual rainfall-runoff models. The obtained results can address the soil moisture monitoring network design from which a reliable soil moisture temporal pattern at the catchment scale can be derived.