Spatial and Temporal Variability of Soil Moisture

The characterization of temporal and spatial variability of soil moisture is highly relevant for understanding the many hydrological processes, to model the processes better and to apply them to conservation planning. Considerable variability in space and time coupled with inadequate and uneven distribution of irrigation water results in uneven yield in an area Spatial and temporal variability highly affect the heterogeneity of soil water, solute transport and leaching of chemicals to ground water. Spatial variability of soil moisture helps in mapping soil properties across the field and variability in irrigation requirement. While the temporal variability of water content and infiltration helps in irrigation management, the temporal correlation structure helps in forecasting next irrigation. Kriging is a geostatistical technique for interpolation that takes into account the spatial auto-correlation of a variable to produce the best linear unbiased estimate. The same has been used for data interpolation for the C. T. A. E. Udaipur India. These interpolated data were plotted against distance to show variability between the krigged value and observed value. The range of krigged soil moisture values was smaller than the observed one. The goal of this study was to map layer-wise soil moisture up to 60 cm depth which is useful for irrigation planning.

[1]  S. G. Reynolds.  A note on the relationship between size of area and soil moisture variability , 1974 .

[2]  Dusanka Zupanski,et al.  Four-Dimensional Variational Data Assimilation for the Blizzard of 2000 , 2002 .

[3]  J. Famiglietti,et al.  Analysis and mapping of field‐scale soil moisture variability using high‐resolution, ground‐based data during the Southern Great Plains 1997 (SGP97) Hydrology Experiment , 2000 .

[4]  Bernd Diekkrüger,et al.  Modelling the spatial variability of soil moisture in a micro-scale catchment and comparison with field data using geostatistics , 2003 .

[5]  D. Mulla,et al.  Estimation of soil properties and wheat yields on complex eroded hills using geostatistics and thematic mapper images , 1991 .

[6]  Bojie Fu,et al.  Geostatistical analysis of soil moisture variability on Da Nangou catchment of the loess plateau, China , 2001 .

[7]  M. Durand,et al.  Effects of uncertainty magnitude and accuracy on assimilation of multiscale measurements for snowpack characterization , 2008 .

[8]  Andrew S. Jones,et al.  Cloud-Resolving Satellite Data Assimilation: Information Content of IR Window Observations and Uncertainties in Estimation , 2006 .

[9]  Andrew W. Western,et al.  The Tarrawarra project: high resolution spatial measurement, modelling and analysis of soil moisture and hydrological response , 1999 .

[10]  András Bárdossy,et al.  Spatial distribution of soil moisture in a small catchment. Part 1: geostatistical analysis , 1998 .

[11]  F. Anctil,et al.  Geostatistics of near-surface moisture in bare cultivated organic soils , 2002 .

[12]  Jennifer L. Dungan,et al.  Kriging in the shadows: Geostatistical interpolation for remote sensing , 1994 .

[13]  Todd D. Ringler,et al.  Initiation of ensemble data assimilation , 2006 .

[14]  R. Reese.  Geostatistics for Environmental Scientists , 2001 .

[15]  Peter A. Troch,et al.  Improved understanding of soil moisture variability dynamics , 2005 .

[16]  W. Crow.  Correcting Land Surface Model Predictions for the Impact of Temporally Sparse Rainfall Rate Measurements Using an Ensemble Kalman Filter and Surface Brightness Temperature Observations , 2003 .

[17]  R. Grayson,et al.  Geostatistical characterisation of soil moisture patterns in the Tarrawarra catchment , 1998 .

[18]  Dani Or,et al.  Spatial and temporal soil water estimation considering soil variability and evapotranspiration uncertainty , 1992 .