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Spatial Variability Of Soil Water Content,particle-size,bulk Density And Their Correlations In Gravel-mulched Fields

Posted on:2019-05-10Degree:MasterType:Thesis
Country:ChinaCandidate:Z CuiFull Text:PDF
GTID:2333330569478231Subject:Water Resources and Hydropower Engineering
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Soil water content(SWC)affects many ecological processes,such as soil erosion,plant growth,and vegetation restoration.As indexes of the physical and chemical properties of soil,soil particle-size distribution(PSD)and soil bulk density(SBD)have large influences on soil infiltration,water-holding capacity,productivity and degradation.We therefore presented a case study of spatial variability of SWC,soil PSD and SBD,and their relationships at 0-50 cm depth in fields mulched with gravel for different periods of time in an arid area of northwestern China using classical statistics and geostatistics.The main conclusions are:(1)We present a case study of the temporal stability and variability of SWC in fields mulched with gravel for different periods of time using maps of the three-dimensional spatial distribution,mean relative differences,and an index of temporal stability.The results showed that the SWCs were much higher in the gravel-mulched fields than bare land and decreased with the length of mulching.SWC of surface soil was more variable,and the variability dropped gradually with depth.The temporal stability of the SWC spatial distribution over time and along the soil profile allowed us to identify locations representative of the field-mean SWCs.The average SWC in the whole study area can be determined over a long period of time from few representative locations.The results are helpful for a rational layout of long-term monitoring locations for SWC in the study area.(2)The boxplots of the field SWC data were drawed,and the percentage increases in the SWCs of the gravel-mulched fields relative to the bare land were calculated.The results showed that the higher SWCs of the gravel-mulched fields than the bare land decreased with depth and length of mulching,indicating the degradation of the fields.Our results will provide a theoretical reference for res olving the degeneration of gravel-mulched field and aid the management of SWC in arid regions.(3)We presented a case study of soil PSD and its relationship with SWC in a gravel-mulched field in an arid area of northwestern China using a statistical regression model,Pearson correlation coefficients and mean absolute error.The results showed that in the depth of 0-50 cm,SWC was correlated significantly with clay content and both increased with soil depth.The regression analysis of clay content and SWC from various depths used the least-squares method.SWCs were significantly positively correlated between soil layers,indicating that the SWC in a specific layer could be well predicted by the SWC in the adjacent subsoil.Clay content can thus be predicted from measured SWC and can be further used to evaluate soil quality and productivity.This result also provides a theoretical reference for the prevention of salinization and the long-term sustainable utilization of gravel-mulched fields.(4)We studied the spatial variability of SBD in gravel-mulched fields of different ages in an arid area of northwestern China using classical statistics,statistical regression,ordinary Kriging and Cokriging.The results showed that the SBDs for a new gravel-mulched field(NGM),a moderately aged gravel-mulched field(MGM),an old gravel-mulched field(OGM)and bare land(CK)decreased in the order OGM>MGM>NGM>CK.The coefficients of variation of SBDs indicated weak spatial variability.SBD was strongly correla ted with SWC,indicating the distribution of the high and the low SBD can be approximately estimated using SWC distribution.Kriging could effectively predict the spatial distribution of SBD in the study area with a high precision of interpolation,but est imating the SBD using Cokriging by measuring the SWC was more accurate.
Keywords/Search Tags:Gravel-mulched field, Soil-water content, Particle-size distribution, Soil bulk density, Spatiotemporal variability, Prediction, Estimation
PDF Full Text Request
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