Spatial patterns and temporal stability of soil moisture across a range of scales in a semi-arid environment

Gómez‐Plaza, A; Álvarez-Rogel, José; Albaladejo, J.; Castillo, V.

doi:10.1002/(sici)1099-1085(200005)14:7<1261::aid-hyp40>3.0.co;2-d

Cited by 187 publications

(45 citation statements)

References 16 publications

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“…In seasonal ecosystems, soil water storage is key for maintaining various site‐specific processes and, ultimately, baseflows during the dry season (Bruijnzeel, ). At site scale, water stored at the unsaturated zone is variable in time and space in mountains (Famiglietti, Rudnicki, & Rodell, ; Famiglietti, Ryu, Berg, Rodell, & Jackson, ; Gómez‐Plaza, Alvarez‐Rogel, Albaladejo, & Castillo, ; Gómez‐Plaza, Martınez‐Mena, Albaladejo, & Castillo, ; Penna, Brocca, Borga, & Dalla Fontana, ). Over time, soil humidity at the unsaturated zone tends to be lower and less stable in the dry than the humid season in different ecosystems (Gómez‐Plaza et al, ; Zhao, Peth, Wang, Lin, & Horn, ).…”

Section: Introductionmentioning

confidence: 99%

Water storage dynamics across different types of vegetated patches in rocky highlands of central Argentina

et al. 2018

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Rivers originating in the granitic highlands of seasonally dry central Argentina provide water to 2 million people. These highlands comprise a complex landscape where a matrix of outcropping rock hosts vegetated patches that vary in response to long‐term grazing and fire. We characterized water storage dynamics across 20 sites representing 6 vegetation‐soil conditions with similar mid‐upslope positions in the landscape. We measured temporal and spatial variability of water inputs (rain and fog) and water stored at the unsaturated and saturated zones. We found that rainfall was highly seasonal, but fog occurred year‐round, likely representing an extra water source in areas with complex vegetation structure. Moreover, fog seems to regulate evapotranspiration/topsoil water dynamics. Water was stored in the saturated zone only during the rainy season. Piezometric response to rainfall was rapid but transient (dropping an average 15 cm/day), possibly buffering peak stormflows, reducing sediment yield, and delivering subsurface water downslope for potential storing throughout the year. Spatially, a reduction in soil depth (from 100 up to 9 cm) and vegetation structure (from woodlands to stonelands), with a degradation of topsoil conditions for infiltration were accompanied by a decline in water storage at the unsaturated (from 32% up to 14%) and saturated (from 46.3 up to 0 cm) zones. Taken together, our results support the infiltration trade‐off hypothesis, which states that vegetation structure benefits fog interception, soil properties that enhance water infiltration, subsurface flow paths, and storage. Long‐term disturbances have likely triggered a degradation of the hydrological function of seasonal highlands in central Argentina.

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Section: Introductionmentioning

confidence: 99%

Water storage dynamics across different types of vegetated patches in rocky highlands of central Argentina

et al. 2018

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“…Dynamic monitoring of soil moisture on the scale of irrigation fields is an essential part of precision agriculture. Despite advances in recent interpolation algorithms, estimation of the spatial distribution of near-surface soil moisture is still unsatisfactory because soil moisture is highly heterogeneous on both the spatial and temporal scales, even over short distances [20]. Many researchers have applied remote sensing to invert soil moisture.…”

Section: Introductionmentioning

confidence: 99%

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

Zhang

Yang

et al. 2017

Sensors

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In the practice of interpolating near-surface soil moisture measured by a wireless sensor network (WSN) grid, traditional Kriging methods with auxiliary variables, such as Co-kriging and Kriging with external drift (KED), cannot achieve satisfactory results because of the heterogeneity of soil moisture and its low correlation with the auxiliary variables. This study developed an Extended Kriging method to interpolate with the aid of remote sensing images. The underlying idea is to extend the traditional Kriging by introducing spectral variables, and operating on spatial and spectral combined space. The algorithm has been applied to WSN-measured soil moisture data in HiWATER campaign to generate daily maps from 10 June to 15 July 2012. For comparison, three traditional Kriging methods are applied: Ordinary Kriging (OK), which used WSN data only, Co-kriging and KED, both of which integrated remote sensing data as covariate. Visual inspections indicate that the result from Extended Kriging shows more spatial details than that of OK, Co-kriging, and KED. The Root Mean Square Error (RMSE) of Extended Kriging was found to be the smallest among the four interpolation results. This indicates that the proposed method has advantages in combining remote sensing information and ground measurements in soil moisture interpolation.

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“…The observed SM from stations is generally considered accurate and regarded as the reference SM (Brocca, Melone, Moramarco, Wagner, & Hasenauer, 2010). However, the strong spatial-temporal heterogeneity (James, Partel, Wilson, & Peltzer, 2003;Chaney, Roundy, Herrera-Estrada, & Wood, 2015;Gómez-Plaza, Alvarez-Rogel, Albaladejo, & Castillo, 2000) and discontinuity of SM data from stations limit its application in the research of spatial patterns and temporal evolution for SM (Clark, 2002;Wolf, 2011). However, the strong spatial-temporal heterogeneity (James, Partel, Wilson, & Peltzer, 2003;Chaney, Roundy, Herrera-Estrada, & Wood, 2015;Gómez-Plaza, Alvarez-Rogel, Albaladejo, & Castillo, 2000) and discontinuity of SM data from stations limit its application in the research of spatial patterns and temporal evolution for SM (Clark, 2002;Wolf, 2011).…”

Section: Introductionmentioning

confidence: 99%

Comparison of soil moisture products from microwave remote sensing, land model, and reanalysis using global ground observations

Deng

Wang

Bai

et al. 2019

Hydrological Processes

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High-quality soil moisture (SM) datasets are in great demand for climate, hydrology, and other fields, but detailed evaluation of SM products from various sources is scarce. Thus, using 670 SM stations worldwide, we evaluated and compared SM products from microwave remote sensing [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) (C-and X-bands) and European Space Agency's Climate Change Initiative (ESA CCI)], land surface model [Global Land Data Assimilation System (GLDAS)], and reanalysis data [ECMWF Re-Analysis-Interim (ERA-Interim) and National Centers for Environmental Prediction (NCEP)] under different time scales and various climates and land covers. We find that: (a) ESA CCI and GLDAS have the closest values to the in situ SM on the annual scale, whereas others overestimate the SM; ERA-Interim (averaged R = 0.58) and ESA CCI (averaged R = 0.54) correlate best with the in situ data, while GLDAS performs worst. (b) Overall, the deviations of each product vary in seasons. ESA CCI and ERA-Interim products are closer to the in situ SM at seasonal scales, and AMSR-E and NCEP perform worst in December-February and June-August, respectively. (c) Except for NCEP and ERA-Interim, others can well reflect the intermonthly variation of the in situ SM. (d) Under various climates and land covers, AMSR-E products are less effective in cold climates, whereas GLDAS and NCEP products perform poorly in arid or temperate and dry climates. Moreover, the Bias and R of each SM product differobviously under different forest types, especially the AMSR-E products. In summary, SM from ESA CCI is the best, followed by ERA-Interim product, and precipitation is an important auxiliary data for selecting high-quality SM stations and improving the accuracy of SM from GLDAS. These results can provide a reference for improving the accuracy of the above SM products. K E Y W O R D S

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Spatial patterns and temporal stability of soil moisture across a range of scales in a semi-arid environment

Cited by 187 publications

References 16 publications

Water storage dynamics across different types of vegetated patches in rocky highlands of central Argentina

Water storage dynamics across different types of vegetated patches in rocky highlands of central Argentina

An Extended Kriging Method to Interpolate Near-Surface Soil Moisture Data Measured by Wireless Sensor Networks

Comparison of soil moisture products from microwave remote sensing, land model, and reanalysis using global ground observations

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