scholarly journals Spatial variability of soil moisture content and its relation to environmental indices in a semi-arid gully catchment of the Loess Plateau, China

2001 ◽  
Vol 49 (4) ◽  
pp. 723-750 ◽  
Author(s):  
Yang Qiu ◽  
Bojie Fu ◽  
Jun Wang ◽  
Liding Chen
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Spatial Variability ◽  
Loess Plateau ◽  
Soil Moisture Content ◽  
The Loess Plateau ◽  
Environmental Indices ◽  
Semi Arid

scholarly journals Spatial-Temporal Simulations of Soil Moisture Content in a Large Basin of the Loess Plateau, China

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhiqi Wang ◽  
Xiaobo Feng ◽  
Zhihong Yao ◽  
Zhaolong Ma ◽  
Guodong Ji
Keyword(s):  
Land Use ◽  
Soil Moisture ◽  
Seasonal Variation ◽  
Moisture Content ◽  
Loess Plateau ◽  
Soil Moisture Content ◽  
Spatial And Temporal Variation ◽  
Slope Aspect ◽  
The Loess Plateau ◽  
Basin Scale

Soil moisture is a crucial factor limiting the growth and survival of plants on the Loess Plateau. Its level has a severe impact on plants’ growth and development and the type and distribution characteristics of communities. This study area is the Jihe Basin in the Loess Plateau, China. Multiple linear regression models with different environmental variables (land use, topographic and meteorological factors, etc.) were developed to simulate soil moisture’s spatial and temporal changes by integrating field experiments, indoor analysis, and GIS spatial analysis. The model performances were evaluated in the Jihe Basin, with soil moisture content measurements. The result shows that soil moisture content is positively correlated with soil bulk density, monthly rainfall, topographic wetness index, land use coefficient, and slope aspect coefficient but negatively correlated with the monthly-averaged temperature and the relative elevation coefficient. The selected variables are all related to the soil moisture content and can account for 75% of the variations of soil moisture content, and the remaining 25% of the variations are related to other factors. Comparing the simulated and measured values at all sampling points shows that the average error of all the simulated values is 0.09, indicating that the simulation has high accuracy. The spatial distribution of soil moisture content is significantly affected by land use and topographic factors, and seasonal variation is remarkable in the year. Seasonal variation of soil moisture content is determined by the seasonal variation of rainfall and the air temperature (determining evaporation) and vegetation growth cycle. Therefore, the proposed model can simulate the spatial and temporal variation of soil moisture content and support developing the soil and water loss model on a basin scale.

scholarly journals Geostatistical analysis for soil moisture content under the no tillage cropping system

2006 ◽  
Vol 63 (4) ◽  
pp. 341-350 ◽  
Author(s):  
Célia Regina Grego ◽  
Sidney Rosa Vieira ◽  
Aline Maria Antonio ◽  
Simone Cristina Della Rosa
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Spatial Variability ◽  
Water Content ◽  
Spatial Dependence ◽  
Soil Moisture Content ◽  
No Tillage ◽  
Moisture Contents ◽  
Classical Statistics ◽  
Evaluation Point

Experiments in agriculture usually consider the topsoil properties to be uniform in space and, for this reason, often make inadequate use of the results. The objective of this study was to assess the variability for soil moisture content using geostatistical techniques. The experiment was carried out on a Rhodic Ferralsol (typic Haplorthox) in Campinas, SP, Brazil, in an area of 3.42 ha cultivated under the no tillage system, and the sampling was made in a grid of 102 points spaced 10 m x 20 m. Access tubes were inserted down to one meter at each evaluation point in order to measure soil moisture contents (cm³ cm-3) at depths of 30, 60 and 90 cm with a neutron moisture gauge. Samplings were made between the months of August and September of 2003 and in January 2004. The soil moisture content for each sampling date was analyzed using classical statistics in order to appropriately describe the central tendency and dispersion on the data and then using geostatistics to describe the spatial variability. The comparison between the spatial variability for different samplings was made examining scaled semivariograms. Water content was mapped using interpolated values with punctual kriging. The semivariograms showed that, at the 60 cm depth, soil water content had moderate spatial dependence with ranges between 90 and 110 m. However, no spatial dependence was found for 30 and 90 cm depths in 2003. Sampling density was insufficient for an adequate characterization of the spatial variability of soil moisture contents at the 30 and 90 cm depths.

scholarly journals Semi-Arid Region Soil Moisture Prediction using Multivariate Regression

2019 ◽  
Vol 8 (4) ◽  
pp. 12457-12460
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Soil Properties ◽  
Arid Region ◽  
Soil Moisture Content ◽  
Linear Regression Analysis ◽  
Organic Content ◽  
Dry Density ◽  
Semi Arid Region ◽  
Semi Arid

The Water Scarcity is a prominent feature in Arid and Semi-Arid region. Soil moisture content is significant factor in deciding vegetation growth and also affects the performance of any water harvesting system in place. This paper evaluates the interrelationship of Soil properties with Soil Moisture content. The study covers about 13 soil Samples from Single Watershed. The soil properties covered in the study are Conductivity, pH, Bulk Density, Dry Density, Specific gravity, organic content, void ratio, and Moisture Content. Multiple linear regression analysis was done to determine significance of each soil properties for soil moisture content as individual and as whole. Modelling was done based on soil characteristics to predict Soil Moisture. Principal Component Analysis was performed to identify most significant soil properties responsible for variation of prediction of Soil Moisture content. The Correlation between location topography and Moisture Content was obtained through Cluster Analysis.

scholarly journals Soil moisture influence on the interannual variation in temperature sensitivity of soil organic carbon mineralization in the Loess Plateau

2015 ◽  
Vol 12 (11) ◽  
pp. 3655-3664 ◽  
Author(s):  
Y. J Zhang ◽  
S. L Guo ◽  
M. Zhao ◽  
L. L. Du ◽  
R. J. Li ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Moisture Content ◽  
Carbon Cycle ◽  
Temperature Sensitivity ◽  
Interannual Variation ◽  
Soil Moisture Content ◽  
The Loess Plateau ◽  
Soc Mineralization

Abstract. Temperature sensitivity of soil organic carbon (SOC) mineralization (i.e., Q10) determines how strong the feedback from global warming may be on the atmospheric CO2 concentration; thus, understanding the factors influencing the interannual variation in Q10 is important for accurately estimating local soil carbon cycle. In situ SOC mineralization rate was measured using an automated CO2 flux system (Li-8100) in long-term bare fallow soil in the Loess Plateau (35°12' N, 107°40' E) in Changwu, Shaanxi, China from 2008 to 2013. The results showed that the annual cumulative SOC mineralization ranged from 226 to 298 g C m−2 yr−1, with a mean of 253 g C m−2 yr−1 and a coefficient of variation (CV) of 13%, annual Q10 ranged from 1.48 to 1.94, with a mean of 1.70 and a CV of 10%, and annual soil moisture content ranged from 38.6 to 50.7% soil water-filled pore space (WFPS), with a mean of 43.8% WFPS and a CV of 11%, which were mainly affected by the frequency and distribution of precipitation. Annual Q10 showed a quadratic correlation with annual mean soil moisture content. In conclusion, understanding of the relationships between interannual variation in Q10, soil moisture, and precipitation are important to accurately estimate the local carbon cycle, especially under the changing climate.

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