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Estimating spatially distributed soil water content at small watershed scales based on decomposition of temporal anomaly and time stability analysis

机译:基于时间异常分解和时间稳定性分析的小流域空间分布土壤含水量估算

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Soil water content (SWC) is crucial to rainfall-runoff response at the watershed scale. A model was used to decompose the spatiotemporal SWC into a time-stable pattern (i.e.,?temporal mean), a space-invariant temporal anomaly, and a space-variant temporal anomaly. The space-variant temporal anomaly was further decomposed using the empirical orthogonal function (EOF) for estimating spatially distributed SWC. This model was compared to a previous model that decomposes the spatiotemporal SWC into a spatial mean and a spatial anomaly, with the latter being further decomposed using the EOF. These two models are termed the temporal anomaly (TA) model and spatial anomaly (SA) model, respectively. We aimed to test the hypothesis that underlying (i.e.,?time-invariant) spatial patterns exist in the space-variant temporal anomaly at the small watershed scale, and to examine the advantages of the TA model over the SA model in terms of the estimation of spatially distributed SWC. For this purpose, a data set of near surface (0–0.2?m) and root zone (0–1.0?m) SWC, at a small watershed scale in the Canadian Prairies, was analyzed. Results showed that underlying spatial patterns exist in the space-variant temporal anomaly because of the permanent controls of istatic/i factors such as depth to the CaCOsub3/sub layer and organic carbon content. Combined with time stability analysis, the TA model improved the estimation of spatially distributed SWC over the SA model, especially for dry conditions. Further application of these two models demonstrated that the TA model outperformed the SA model at a hillslope in the Chinese Loess Plateau, but the performance of these two models in the GENCAI network (~??250?kmsup2/sup) in Italy was equivalent. The TA model can be used to construct a high-resolution distribution of SWC at small watershed scales from coarse-resolution remotely sensed SWC products.
机译:在分水岭范围内,土壤水分(SWC)对于降雨-径流响应至关重要。使用模型将时空SWC分解为时间稳定的模式(即时间均值),时不变的时空异常和时变的时空异常。使用经验正交函数(EOF)进一步分解时空时空异常,以估计空间分布的SWC。将该模型与先前的模型进行了比较,该模型将时空SWC分解为空间均值和空间异常,并使用EOF将其进一步分解。这两个模型分别称为时间异常(TA)模型和空间异常(SA)模型。我们旨在检验以下假设:在小分水岭尺度上,时变空间异常中存在潜在的(即“时不变”)空间模式,并在估计方面检验了TA模型相对于SA模型的优势空间分布的SWC。为此,在加拿大大草原地区以小流域尺度分析了近地表(0〜0.2?m)和根区(0〜1.0?m)SWC的数据集。结果表明,由于静态因子的永久控制,例如CaCO 3 层的深度和有机碳含量,永久存在于时空异常中的潜在空间模式。结合时间稳定性分析,TA模型相对于SA模型改善了空间分布SWC的估计,尤其是在干燥条件下。这两个模型的进一步应用表明,在中国黄土高原的一个山坡上,TA模型的性能优于SA模型,但是这两个模型在GENCAI网络中的性能(〜?250?km 2 )在意大利是等效的。 TA模型可用于从粗糙分辨率的遥感SWC产品构建小分水岭规模的SWC高分辨率分布。

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