Factors regulating spatial distribution of soil water content in small semiarid catchments

Gómez‐Plaza, A; Martínez-Mena, María; Albaladejo, J.; Castillo, V.

doi:10.1016/s0022-1694(01)00483-8

Cited by 297 publications

(231 citation statements)

References 23 publications

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“…Often, wetter conditions corresponded to greater σ 2 n (S tn ), as further indicated by moderate correlation between σ 2 n (S tn ) and S tn (R 2 of 0.51 and 0.38 for the near surface and the root zone, respectively). This was in agreement with others (Gómez-Plaza et al, 2001;Martínez-Fernández and Ceballos, 2003;Hu et al, 2011). Furthermore, there were greater σ 2 n (S tn ) values at the near surface than in the root zone, indicating greater variability of SWC in the near surface.…”

Section: Time-stable Pattern (Mt N ) Space-invariantsupporting

confidence: 92%

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

Cheng

2016

Hydrol. Earth Syst. Sci.

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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.

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Section: Time-stable Pattern (Mt N ) Space-invariantsupporting

confidence: 92%

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

Cheng

2016

Hydrol. Earth Syst. Sci.

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“…For example, several hydrological models have been applied to test the effect of the spatial distribution of a hydrological variable (e.g. specific discharge, soil moisture, or groundwater 70 recharge) (Erickson et al, 2005;Gómez-Plaza et al, 2001;Li et al, 2014). However, the effects of topography alone on hydrology are not usually addressed in those studies.…”

Section: Introductionmentioning

confidence: 99%

Topography significantly influencing low flows in snow-dominated watersheds

Li¹,

Wei²,

Yang³

et al. 2017

Preprint

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and other environmental fields. Flow variables include annual mean flow (Qmean), Q10%, Q25%, Q50%, Q75%, Q90%, and annual minimum flow (Qmin), where Qx% is defined as flows that at the percentage (x) occurred in any given year. Factor analysis (FA) was first adopted to exclude some redundant or repetitive TIs. Then, stepwise regression models were employed to quantify the relative contributions of TIs to each flow variable in each year. Our results show that topography plays a more important 30 role in low flows than high flows. However, the effects of TIs on flow variables are not consistent.Our analysis also determines five significant TIs including perimeter, surface area, openness, terrain characterization index, and slope length factor, which can be used to compare watersheds when low flow assessments are conducted, especially in snow-dominated regions. 35

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“…Soil moisture is highly and nonlinearly variable in both time and space due to heterogeneity of climate and soil across different scales (Heathman et al, 2012). Studies have identified important spatiotemporal variability characteristics of SWC from different aspects, such as the spatio-temporal patterns of SWC (Cantón et al, 2004;Brocca et al, 2007), the relationship between the heterogeneity and its mean values (Hupet and Vanclooster, 2002;Choi and Jacobs, 2007), the spatial scaling of SWC (Western and Blöschl, 1999;Gao and Shao, 2012a), and the controlling factors of spatio-temporal patterns (Gómez-Plaza et al, 2001;Zhao et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Spatio-temporal variability and temporal stability of water contents distributed within soil profiles at a hillslope scale

Gao

Shao

Peng

et al. 2015

CATENA

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a b s t r a c t a r t i c l e i n f oInformation about soil water content (SWC) within soil profiles in terms of its spatio-temporal variability and temporal stability is crucial when selecting appropriate soil water management practices. However, detailed profile distribution features for related indices are not clear on the Chinese Loess Plateau. This study aimed to investigate the depth dependency of spatio-temporal variability and temporal stability of SWC at the hillslope scale using an intensive sampling strategy for both horizontal and vertical directions as well as over time. The SWCs at 20 depths within 0-300 cm soil profiles were measured on 20 occasions between July 2008 and October 2010 at 91 locations on a hillslope on the Loess Plateau, China. Results showed that although the profile distributions of investigated statistical parameters differed greatly, they were all depth dependent. Based on both the spatio-temporal variability and the temporal stability characteristics using eight indices, the studied soil profile (0-300 cm) could be divided into three soil layers, i.e., 0-60, 60-160 and 160-300 cm, with a characteristic feature of "irregularly changing", "regularly changing", and "relatively constant", respectively. The deepest rainwater replenishment depth was approximately 160 cm. Therefore, choosing 200 cm as the sampling depth would be sufficient in similar areas. Such findings are useful for designing an optimal strategy for sampling and management of profile soil moisture.

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Factors regulating spatial distribution of soil water content in small semiarid catchments

Cited by 297 publications

References 23 publications

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

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

Topography significantly influencing low flows in snow-dominated watersheds

Spatio-temporal variability and temporal stability of water contents distributed within soil profiles at a hillslope scale

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