Application of temporal stability analysis in depth-scaling estimated soil water content by cosmic-ray neutron probe on the northern Tibetan Plateau

Zhu, Xuchao; Shao, Mingan; Jia, Xiaoxu; Huang, Laiming; Zhu, Juntao; Zhang, Yangjian

doi:10.1016/j.jhydrol.2017.01.019

Cited by 20 publications

(19 citation statements)

References 37 publications

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“…First of all, when recommending additional profile measurements, it is important to underline that the goal of these additional measurements is not to find the absolute soil moisture values or the time-stable locations that best represent the average soil moisture of the field site. Of course, having a single time-stable location to complement the CRNS measurements (as determined in Nguyen et al, 2017Nguyen et al, , 2019Zhu et al, 2017) could be an advantage. However, this would require additional prior survey because it is not possible to determine such a location before the installation of the point sensors (Vanderlinden et al, 2012).…”

Section: Selection Of the Locations To Measure The Soil Moisture Profilementioning

confidence: 99%

A profile shape correction to reduce the vertical sensitivity of cosmic‐ray neutron sensing of soil moisture

Scheiffele

Baroni

Franz

et al. 2020

Vadose Zone Journal

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In recent years, cosmic-ray neutron sensing (CRNS) has shown a large potential among proximal sensing techniques to monitor soil moisture noninvasively, with high frequency and a large support volume (radius up to 240 m and sensing depth up to 80 cm). This signal is, however, more sensitive to closer distances and shallower depths. Inherently, CRNS-derived soil moisture is a spatially weighted value, different from an average soil moisture as retrieved by a sensor network. In this study, we systematically test a new profile shape correction on CRNS-derived soil moisture, based on additional soil moisture profile measurements and vertical unweighting, which is especially relevant during pronounced wetting or drying fronts. The analyses are conducted with data collected at four contrasting field sites, each equipped with a CRNS probe and a distributed soil moisture sensor network. After applying the profile shape correction on CRNS-derived soil moisture, it is compared with the sensor network average. Results show that the influence of the vertical sensitivity of CRNS on integral soil moisture values is successfully reduced. One to three properly located profile measurements within the CRNS support volume improve the performance. For the four investigated field sites, the RMSE decreased 11-53% when only one profile location was considered. We therefore recommend to install along with a CRNS at least one soil moisture profile in a radial distance <100 m and a measurement depth down to 50 cm. Profile-shape-corrected, CRNS-derived soil moisture is an unweighted integral soil moisture over the support volume, which is easier to interpret and easier to use for further applications.

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Section: Selection Of the Locations To Measure The Soil Moisture Profilementioning

confidence: 99%

A profile shape correction to reduce the vertical sensitivity of cosmic‐ray neutron sensing of soil moisture

Scheiffele

Baroni

Franz

et al. 2020

Vadose Zone Journal

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“…Li et al () studied the spatial variability of SWC on a mesoscale using a geostatistical approach in a desertified aeolian riparian ecotone on the Tibetan Plateau. Zhu et al () and Zhu, Cao, Shao, and Liang () studied the temporal variation of mean SWC in an alpine meadow on the northern Tibetan Plateau on a hectometer scale using the newly developed cosmic‐ray neutron probe (NP). The spatial variability of SWC and the temporal variation of the spatial variability, however, remains unknown.…”

Section: Introductionmentioning

confidence: 99%

Mesoscale spatial variability of soil‐water content in an alpine meadow on the northern Tibetan Plateau

Zhu

Shao

Liang

et al. 2019

Hydrological Processes

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Soil water is an important limiting factor for restoring alpine meadows on the northern Tibetan Plateau. Field studies of soil‐water content (SWC), however, are rare due to the harsh environment, especially in a mesoscale alpine‐meadow ecosystem. The objective of this study was to assess the spatial variability of SWC and the temporal variation of the spatial variability in a typical alpine meadow using a geostatistical approach. SWC was measured using a neutron probe to a depth of 50 cm at 113 locations on 22 sampling occasions in a 33.5‐hm2 alpine meadow during the 2015 and 2016 growing seasons. Mean SWC in the study plot for the two growing seasons was 18.7, 14.0, 13.9, 14.3, and 14.8% for depths of 10, 20, 30, 40, and 50 cm, respectively, and SWC was significantly larger at 10 cm than at other depths. SWC was negatively correlated with its spatial variability, and the spatial variability was higher when SWC was lower. Thirty‐three sampling locations in this study plot met the requirement of accuracy of the central limit theorem. A Gaussian model was the best fit for SWC semivariance at depths of 10, 20, and 30 cm, and the spatial structural ratio was between 0.997 and 1, indicating a strong spatial dependence of SWC. The sill and range fluctuated temporally, and the nugget and spatial structural ratio did not generally vary with time. The sill was significantly positively correlated with SWC and was initially stable and then tend to increase with SWC. The nugget, range, and spatial structure ratio, however, were not correlated with SWC. These results contribute to our understanding of SWC spatial distribution and variation in alpine meadows and provide basic empirical SWC data for mesoscale model simulations, optimizing sampling strategies and managing meadows on the Tibetan Plateau.

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“…SWC‐D was notably similar to SWC‐W at a depth of 50 cm, which may indicate that the 50‐cm‐thick alpine meadow soil had some buffering capacity to cope with extreme weather conditions. Previous studies have also shown that the SWC at this 50‐cm depth was relatively stable and minimally influenced by the external environment (weather, vegetation, and terrain) in this alpine meadow (Zhu et al, ; Zhu, Shao, & Liang, ). This stability was partly determined by the vegetation characteristics of the meadow.…”

Section: Resultsmentioning

confidence: 63%

“…SWC studies at hectometer scales in this area are rare but are needed to fill the gap between point and regional scales and to provide a bridge for scale transformation. Zhu et al (), Zhu et al (), and Zhu, Cao, Shao, and Liang () studied the temporal variation in SWC at a hectometer scale using a cosmic‐ray neutron probe to measure SWC, but the mean SWC could not reflect the spatial variability. The SWC in alpine meadows on the TP has also been simulated using SHAW and CoupModel (Chen et al, ; Zhang et al, ).…”

Section: Introductionmentioning

confidence: 99%

Spatial simulation of soil‐water content in dry and wet conditions in a hectometer‐scale degraded alpine meadow

2018

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Soil‐water content (SWC) is a key factor in restoring degraded vegetation in alpine meadow ecosystems, but it has rarely been spatially simulated on a hectometer scale. We simulated SWC for typical dry and wet days in an alpine meadow using multivariate linear regression and autoregressive state‐space equations based on SWC and other soil, terrain, and vegetation parameters to evaluate the efficiency of these two methods in dry and wet soil‐moisture conditions. SWC measured on a typical dry day (SWC‐D) and a wet day (SWC‐W) increased and decreased with depth, respectively, and SWC‐D was similar to SWC‐W at a depth of 50 cm. Both SWC‐D and SWC‐W were significantly correlated with soil bulk density (BD), capillary porosity, silt content (Silt), gravel and stone content (GSC), pH, and organic carbon density (OCD), and both SWC‐D and SWC‐W were significantly auto‐correlated and cross‐correlated with BD, Silt, GSC, pH, and OCD at more than one lag distance. Multivariate linear regression using three variables in both dry and wet conditions had the highest accuracy, and the accuracy was generally higher for dry conditions than it was for wet conditions. The bivariate state‐space model was the most accurate for both dry and wet soil conditions, but the expression variables were totally different, with pH and OCD for dry day and BD and Silt for wet day. The conditions of soil moisture should thus be considered when choosing variables with which to simulate SWC, instead of only considering the relationships between SWC and other variables.

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Application of temporal stability analysis in depth-scaling estimated soil water content by cosmic-ray neutron probe on the northern Tibetan Plateau

Cited by 20 publications

References 37 publications

A profile shape correction to reduce the vertical sensitivity of cosmic‐ray neutron sensing of soil moisture

A profile shape correction to reduce the vertical sensitivity of cosmic‐ray neutron sensing of soil moisture

Mesoscale spatial variability of soil‐water content in an alpine meadow on the northern Tibetan Plateau

Spatial simulation of soil‐water content in dry and wet conditions in a hectometer‐scale degraded alpine meadow

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