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

Abstract

Abstract. Soil water content (SWC) is crucial to rainfallrunoff 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 spacevariant 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 static factors such as depth to the CaCO 3 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 km 2 ) in Italy was equivalent. The TA model can be used to construct a high-resolution distribution of SWC at small watershed scales from coarseresolution remotely sensed SWC products.

Spatio-temporal variability in sea surface temperatures for the Yellow Sea based on MODIS dataset

“…Recently, several analyses of SST datasets have considered the spatial and temporal distributions of SST in order to examine seasonal and annual SST variability and the warming signal in the YS. These studies used the empirical orthogonal function (EOF) method (Yeh and Kim 2010;Park et al 2015), which is also widely used in other disciplines (Alvarez and Pan 2016;Hu and Si 2016). These earlier studies of SST variability consider the YS as an area in its entirety and focus on relatively long time series variations.…”

Spatiotemporal Variability of Monthly and Annual Snow Depths in Xinjiang, China over 1961–2015 and the Potential Effects

“…A number of the methods could reveal the internal spatiotemporal structures of the hydrological time series, such as stochastic method, singular value decomposition, canonical correlation, cluster analysis, empirical mode decomposition [13], artificial neural networks [14], support vector machine [15], spectrum analysis [16], wavelet analysis [17], empirical orthogonal function (EOF) [18], etc.The EOF method condenses the spatiotemporal information, reveals spatial and systematic structures, and identifies dominant variation patterns of series. It has been applied for soil water content variability in the Canadian Prairie pothole region [19], water quality variation of Nakdong River, Korea [20], terrestrial water storage change in the Tarim River basin, China [21], daily snow depth changes in Xinjiang, China [6] and other hydrological variables [22,23]. The wavelet analysis includes continuous and discrete ones.…”

“…The EOF method condenses the spatiotemporal information, reveals spatial and systematic structures, and identifies dominant variation patterns of series. It has been applied for soil water content variability in the Canadian Prairie pothole region [19], water quality variation of Nakdong River, Korea [20], terrestrial water storage change in the Tarim River basin, China [21], daily snow depth changes in Xinjiang, China [6] and other hydrological variables [22,23]. The wavelet analysis includes continuous and discrete ones.…”

Seasonal to interannualvariability of Chlorophyll-<i>a</i> and sea surface temperature in the Yellow Sea using MODIS satellite datasets

“…After DINEOF reconstruction, cloud free CHL values were log-transformed before we included them in figures and before statistical analysis. In Section 4, to better discern the spatial heterogeneity and the degree of coherence and temporal evolution of the CHL and SST fields, a traditional EOF analysis was applied further to the monthly, cloud-free DINEOF CHL and SST datasets, which is an approach that is also used widely in other disciplines (Hu and Si, 2016a). Each data set was organized in an M×N matrix, where M and N represented the spatial and temporal elements, respectively.…”