An in situ probe-spacing-correction thermo-TDR sensor to measure soil water content accurately

2018 ◽  
Vol 69 (6) ◽  
pp. 1030-1034 ◽  
Author(s):  
M. M. Wen ◽  
G. Liu ◽  
R. Horton ◽  
K. Noborio
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Probe Spacing

Definition of soil water content and rainfall thresholds for landslide occurrence

2020 ◽  
Author(s):  
Luca Piciullo ◽  
Graham Gilbert
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Climate Adaptation ◽  
Early Warning Systems ◽  
In Situ Measurements ◽  
Physical Parameters ◽  
Rainfall Thresholds ◽  
Reanalysis Dataset

<p>In the last decades, rainfall thresholds for landslide occurrences were thoroughly investigated, producing several different test cases and relevant technical and scientific advances. However, a recent literature review on rainfall thresholds articles (Segoni et al., 2018), published in journals indexed in SCOPUS or ISI Web of knowledge databases in the period 2008-2016, highlighted significant advances and critical issues about this topic. Only in the 11% of the analysed papers (a total of 115) there were installed instruments for measuring physical parameters other than rainfall. The implication was that, in most cases, the occurrence of landslides was forecasted considering exclusively a rainfall correlation, completely neglecting soil characteristics.</p><p>A reanalysis dataset (ERA5-Land) providing a consistent view of the evolution of land variables over several decades at an enhanced resolution has been used to evaluate the soil water content. Reanalysis combines numerical model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. A comparison between in situ measurements with the results of the model has been carried out for two sites in Norway (Eidsvoll, Morsa catchmen) with 3 different vegetation types: grass, bush, tree. The results showed a good agreement between the modelled soil water content layer 2 and 3 (respectively representing 2 - 28 cm and 28 -100 cm depths) and, respectively, in-situ measurements at 30 and 50 cm depths.</p><p>Then, 15 Norwegian basins with moraine and peat covers and, previous landslide occurrences in the period 2009-2018, have been selected for correlations. Combinations of rainfall and soil water contents that triggered and not-triggered landslides have been analysed. Rainfall-soil water content thresholds have been defined for the selected basins highlighting the important role played by soil water content, together with rainfall, in triggering landslides. The use of the soil water content contributed to increase the performance of the thresholds and to reduce the uncertainties of landslide forecast.</p><p>This paper has been conceived in the context of the project "Klima 2050-Risk reduction through climate adaptation of buildings and infrastructure" http://www.klima2050.no/, and it is included into Work Package 3.3-Early warning systems.</p><p> </p>

Field calibration of ThetaProbe (ML2x) and ECHO probe (EC-20) soil water sensors in a Black Vertosol

Soil Research ◽  
2007 ◽  
Vol 45 (3) ◽  
pp. 233 ◽  
Author(s):  
J. L. Foley ◽  
E. Harris
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Clay Soils ◽  
Deep Drainage ◽  
Swelling Clay ◽  
Field Calibration ◽  
Volumetric Measurements

Past studies have shown that soil-specific calibrations are required to attain a higher level of accuracy when measuring soil water content with ThetaProbe and ECHO probe soil water sensors, particularly in swelling clay soils. Both probes were assessed for their capacity to accurately monitor soil water in a deep drainage study on a Black Vertosol. Probes were trialled in situ and calibrated against hand-sampled volumetric measurements. The generic calibrations given by the manufacturers resulted in significant errors in water content estimates for both probes. Using the generic calibration, ECHO probes under-estimated water content by 0.10–0.2 m3/m3, whereas ThetaProbes under-estimated by 0.04 m3/m3 at the wet end and over-estimated by 0.08 m3/m3 at the dry end. The soil-specific calibrations significantly improved the accuracy of both probes. ThetaProbes were chosen for the drainage study. The calibration allowed for accuracy across the full wet–dry range to within 0.001–0.004 m3/m3 of volumetric measurements. ECHO probes were less accurate at the wet end, but still determined soil water content to within 0.02–0.05 m3/m3 of volumetric measurements.

Evaluating Soil Water Content in a WRF-Noah Downscaling Experiment

2013 ◽  
Vol 52 (10) ◽  
pp. 2312-2327 ◽  
Author(s):  
Peter Greve ◽  
Kirsten Warrach-Sagi ◽  
Volker Wulfmeyer
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Root Zone ◽  
Regional Scale ◽  
Land Surface Model ◽  
Surface Model ◽  
Flux Partitioning

AbstractSoil water content (SWC) depends on and affects the energy flux partitioning at the land–atmosphere interface. Above all, the latent heat flux is limited by the SWC of the root zone on one hand and radiation on the other. Therefore, SWC is a key variable in the climate system. In this study, the performance of the Weather Research and Forecasting model coupled with the Noah land surface model (WRF-Noah) system in a climate hindcast simulation from 1990 to 2008 is evaluated with respect to SWC versus two reanalysis datasets for Europe during 2007 and 2008 with in situ soil moisture observations from southern France. When compared with the in situ observations, WRF-Noah generally reproduces the SWC annual cycle while the reanalysis SWCs do not. The biases in areal mean WRF-Noah SWCs relate to biases in precipitation and evapotranspiration in a cropland environment. The spatial patterns and temporal variability of the seasonal mean SWCs from the WRF-Noah simulations and from the two reanalyses correspond well, while absolute values differ significantly, especially at the regional scale.

Quasi-distributed fiber-optic in-situ monitoring technology for large-scale measurement of soil water content and its application

2021 ◽  
Vol 294 ◽  
pp. 106373
Author(s):  
Meng-Ya Sun ◽  
Bin Shi ◽  
Cheng-Cheng Zhang ◽  
Xing Zheng ◽  
Jun-Yi Guo ◽  
...  
Keyword(s):  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Large Scale ◽  
Fiber Optic ◽  
In Situ Monitoring ◽  
Monitoring Technology

scholarly journals Imaging Rhizosphere CO2 and O2 Concentration to Localize Respiration Hotspots Linked to Root Type and Soil Moisture Dynamics

2021 ◽  
Author(s):  
Sarah Bereswill ◽  
Nicole Rudolph-Mohr ◽  
Sascha E Oswald
Keyword(s):  
Soil Moisture ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Lateral Roots ◽  
Lupinus Albus ◽  
Spatiotemporal Variability ◽  
Cluster Roots ◽  
Small Scale

Abstract PurposeRhizosphere respiration strongly affects CO2 concentration within vegetated soils and resulting fluxes to the atmosphere. Respiration in the rhizosphere exhibits high spatiotemporal variability that may be linked to root type, but also to small-scale variation of soil water content altering gas transport dynamics in the soil. We address spatiotemporal dynamics of CO2 and O2 concentration in the rhizosphere via non-invasive in-situ imaging.MethodsOptodes sensitive to CO2 and O2 were applied to non-invasively measure in-situ rhizosphere CO2 and O2 concentration of white lupine (Lupinus albus) grown in slab-shaped glass rhizotrons. We monitored CO2 concentration over the course of 16 days at constant water content and also performed a drying-rewetting experiment to explore sensitivity of CO2 and O2 concentration to soil moisture changes. ResultsHotspots of respiration formed around cluster roots and CO2 concentration locally increased to > 20 % pCO2 (CO2 partial pressure). After rewetting the soil, cluster roots consumed available O2 significantly faster compared to non-cluster lateral roots. In wet soil, CO2 accumulation zones extended up to 9.5 mm from the root surface compared to 0.3-1 mm in dry soil.ConclusionResults from this imaging experiment indicate that respiratory activity differs substantially within the root system of a plant individual and that cluster roots are hotspots of respiration. As rhizosphere CO2 and O2 concentration was strongly sensitive to soil water content and its variation, we recommend monitoring the soil water content prior and during the measurement of rhizosphere respiration.

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