In situ measurement of soil moisture and pore-water pressures in an ‘incipient’ landslide: Lake Tutira, New Zealand

2011 ◽  
Vol 92 (2) ◽  
pp. 266-274 ◽  
Author(s):  
Richard Hawke ◽  
Jack McConchie
2018 ◽  
Vol 337 ◽  
pp. 210-219 ◽  
Author(s):  
Xianqiang Tang ◽  
Qingyun Li ◽  
Zhenhua Wang ◽  
Yanping Hu ◽  
Yuan Hu ◽  
...  

2019 ◽  
Vol 11 (2) ◽  
pp. 133-141 ◽  
Author(s):  
Chengrong Guo ◽  
Mingjie Ma ◽  
Dongxing Yuan ◽  
Yongming Huang ◽  
Kunning Lin ◽  
...  

A novel sensor system, which integrated sampling, enrichment, and in situ measurement of dissolved Fe(ii) in sediment pore water, was developed.


1985 ◽  
Vol 22 (1) ◽  
pp. 69-78 ◽  
Author(s):  
J. Graham ◽  
V. C. S. Au

Weathering processes such as softening and freeze–thaw cycling affect the properties of clays. Care must therefore be taken when selecting strength and compressibility parameters for analysis of natural slopes, compacted clay embankments, and trench excavations in which significant proportions of the cross section can be affected by climatic weathering.Samples of plastic Lake Agassiz clay from Winnipeg were consolidated anisotropically in the laboratory to axial stresses less than or equal to the in situ effective overburden pressure. They were therefore all overconsolidated with respect to the field preconsolidation pressure. The samples were then loaded under drained or undrained conditions along steeply rising stress paths in p′, q stress space. One group of samples was tested immediately to identify the "undisturbed" behavior, a second group was subjected to freeze–thaw cycles, and a third group allowed to swell freely before testing.The freeze–thaw cycling produced increased compressibility and pore-water pressures, and reduced strengths at low stresses compared with the behavior of undisturbed clay. Freezing also caused the development of a clearly defined fissure structure. Softening at low stresses with access to water produced less marked effects. Key words: clay, undisturbed, freeze–thaw, softening, strength, yielding, pore-water pressures.


2004 ◽  
Vol 293 (1-4) ◽  
pp. 85-99 ◽  
Author(s):  
Jeffrey P Walker ◽  
Garry R Willgoose ◽  
Jetse D Kalma

2017 ◽  
Vol 9 (6) ◽  
pp. 580 ◽  
Author(s):  
Christian N. Koyama ◽  
Hai Liu ◽  
Kazunori Takahashi ◽  
Masanobu Shimada ◽  
Manabu Watanabe ◽  
...  

2021 ◽  
Vol 25 (3) ◽  
pp. 1569-1586
Author(s):  
Jianxiu Qiu ◽  
Jianzhi Dong ◽  
Wade T. Crow ◽  
Xiaohu Zhang ◽  
Rolf H. Reichle ◽  
...  

Abstract. The Soil Moisture Active Passive (SMAP) Level-4 (L4) product provides global estimates of surface soil moisture (SSM) and root-zone soil moisture (RZSM) via the assimilation of SMAP brightness temperature (Tb) observations into the NASA Catchment Land Surface Model (CLSM). Here, using in situ measurements from 2474 sites in China, we evaluate the performance of soil moisture estimates from the L4 data assimilation (DA) system and from a baseline “open-loop” (OL) simulation of CLSM without Tb assimilation. Using random forest regression, the efficiency of the L4 DA system (i.e., the performance improvement in DA relative to OL) is attributed to eight control factors related to the CLSM as well as τ–ω radiative transfer model (RTM) components of the L4 system. Results show that the Spearman rank correlation (R) for L4 SSM with in situ measurements increases for 77 % of the in situ measurement locations (relative to that of OL), with an average R increase of approximately 14 % (ΔR=0.056). RZSM skill is improved for about 74 % of the in situ measurement locations, but the average R increase for RZSM is only 7 % (ΔR=0.034). Results further show that the SSM DA skill improvement is most strongly related to the difference between the RTM-simulated Tb and the SMAP Tb observation, followed by the error in precipitation forcing data and estimated microwave soil roughness parameter h. For the RZSM DA skill improvement, these three dominant control factors remain the same, although the importance of soil roughness exceeds that of the Tb simulation error, as the soil roughness strongly affects the ingestion of DA increments and further propagation to the subsurface. For the skill of the L4 and OL estimates themselves, the top two control factors are the precipitation error and the SSM–RZSM coupling strength error, both of which are related to the CLSM component of the L4 system. Finally, we find that the L4 system can effectively filter out errors in precipitation. Therefore, future development of the L4 system should focus on improving the characterization of the SSM–RZSM coupling strength.


1989 ◽  
Vol 29 (4) ◽  
pp. 75-90 ◽  
Author(s):  
Kenji Ishihara ◽  
Takahito Muroi ◽  
Ikuo Towhata

2011 ◽  
Vol 45 (9) ◽  
pp. 4053-4059 ◽  
Author(s):  
Amy M. P. Oen ◽  
Elisabeth M. L. Janssen ◽  
Gerard Cornelissen ◽  
Gijs D. Breedveld ◽  
Espen Eek ◽  
...  

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