Effect of antecedent soil moisture content on soil critical shear stress in agricultural watersheds

Geoderma ◽  
2016 ◽  
Vol 262 ◽  
pp. 165-173 ◽  
Author(s):  
Harsh Vardhan Singh ◽  
Anita M. Thompson
2020 ◽  
Vol 63 (4) ◽  
pp. 1123-1131
Author(s):  
Anish Khanal ◽  
Garey A. Fox ◽  
Lucie Guertault

HighlightsThe jet erosion test (JET) remains the most commonly used instrument for measuring in situ erodibility.This research investigated the impact of soil moisture content below saturation on erodibility parameters.Erodibility parameters were derived for both linear and nonlinear detachment models.Higher soil moisture increased initial resistance to erosion but also increased erosion rate.Abstract. The jet erosion test (JET) is a commonly employed technique to measure the erodibility of soils in situ by estimating the parameters of linear and nonlinear cohesive sediment detachment models. However, additional research is needed to understand the effect of soil moisture, a critical in situ test condition, on the derived erodibility parameters. This study compared the erodibility parameters, i.e., critical shear stress (tc) and the erodibility coefficient (kd) for the linear excess shear stress equation and two parameters (b0 and b1) for a nonlinear detachment model, from laboratory JETs across two soil types with contrasting texture and moisture contents. The general pattern was that higher soil moisture content increased the soil’s initial resistance to erosion (i.e., higher tc and b1), but once erosion was initiated the rate of erosion was greater (i.e., higher kd and b0). The magnitude of the changes in the erodibility parameters across the three soil moisture profiles investigated in this research were statistically significant, with kd and b0 varying by as much as a factor of 3. This research also confirmed the greater impact of soil moisture content on kd and b0 as compared to tc and b1. For the range of shear stress applied during these JETs, a linear detachment model was more appropriate for the sandy loam soil but less so for the more cohesive clay loam soil, but results were limited to a narrow range in applied shear stress. The results further support existing research conclusions that in situ erodibility measurements obtained under one set of soil moisture conditions may need to be adjusted to better predict soil detachment during storm events. Keywords: Cohesive soil, Critical shear stress, Detachment model, Erodibility, Jet erosion test, Shear stress, Soil moisture.


2019 ◽  
Vol 82 (12) ◽  
pp. 2023-2037 ◽  
Author(s):  
DEBBIE LEE ◽  
MOUKARAM TERTULIANO ◽  
CASEY HARRIS ◽  
GEORGE VELLIDIS ◽  
KAREN LEVY ◽  
...  

ABSTRACT Nearly one-half of foodborne illnesses in the United States can be attributed to fresh produce consumption. The preharvest stage of production presents a critical opportunity to prevent produce contamination in the field from contaminating postharvest operations and exposing consumers to foodborne pathogens. One produce-contamination route that is not often explored is the transfer of pathogens in the soil to edible portions of crops via splash water. We report here on the results from multiple field and microcosm experiments examining the potential for Salmonella contamination of produce crops via splash water, and the effect of soil moisture content on Salmonella survival in soil and concentration in splash water. In field and microcosm experiments, we detected Salmonella for up to 8 to 10 days after inoculation in soil and on produce. Salmonella and suspended solids were detected in splash water at heights of up to 80 cm from the soil surface. Soil-moisture conditions before the splash event influenced the detection of Salmonella on crops after the splash events—Salmonella concentrations on produce after rainfall were significantly higher in wet plots than in dry plots (geometric mean difference = 0.43 CFU/g; P = 0.03). Similarly, concentrations of Salmonella in splash water in wet plots trended higher than concentrations from dry plots (geometric mean difference = 0.67 CFU/100 mL; P = 0.04). These results indicate that splash transfer of Salmonella from soil onto crops can occur and that antecedent soil-moisture content may mediate the efficiency of microbial transfer. Splash transfer of Salmonella may, therefore, pose a hazard to produce safety. The potential for the risk of splash should be further explored in agricultural regions in which Salmonella and other pathogens are present in soil. These results will help inform the assessment of produce safety risk and the development of management practices for the mitigation of produce contamination. HIGHLIGHTS


2011 ◽  
Vol 28 (1) ◽  
pp. 85-91 ◽  
Author(s):  
Run-chun LI ◽  
Xiu-zhi ZHANG ◽  
Li-hua WANG ◽  
Xin-yan LV ◽  
Yuan GAO

2001 ◽  
Vol 66 ◽  
Author(s):  
M. Aslanidou ◽  
P. Smiris

This  study deals with the soil moisture distribution and its effect on the  potential growth and    adaptation of the over-story species in north-east Chalkidiki. These  species are: Quercus    dalechampii Ten, Quercus  conferta Kit, Quercus  pubescens Willd, Castanea  sativa Mill, Fagus    moesiaca Maly-Domin and also Taxus baccata L. in mixed stands  with Fagus moesiaca.    Samples of soil, 1-2 kg per 20cm depth, were taken and the moisture content  of each sample    was measured in order to determine soil moisture distribution and its  contribution to the growth    of the forest species. The most important results are: i) available water  is influenced by the soil    depth. During the summer, at a soil depth of 10 cm a significant  restriction was observed. ii) the    large duration of the dry period in the deep soil layers has less adverse  effect on stands growth than in the case of the soil surface layers, due to the fact that the root system mainly spreads out    at a soil depth of 40 cm iii) in the beginning of the growing season, the  soil moisture content is    greater than 30 % at a soil depth of 60 cm, in beech and mixed beech-yew  stands, is 10-15 % in    the Q. pubescens  stands and it's more than 30 % at a soil depth of 60 cm in Q. dalechampii    stands.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Rehman S. Eon ◽  
Charles M. Bachmann

AbstractThe advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.


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