Spatial and Temporal Variability of Soil Moisture
Vanita Pandey¹, Pankaj K. Pandey²*
¹Department of Soil and Water Engineering, CAEPHT, Central Agricultural University, Gangtok, India
²Department of Agricultural Engineering, North Eastern Regional Institute of Science & Technology, Nirjuli Itanagar Arunachal Pradesh, India
E-mail: pandeypk@gmail.com
Received February 11, 2010; revised March 15, 2010; accepted April 20, 2010
Abstract
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.
Keywords: Soil Moisture, Spatial & Temporal Variability, Kriging
1. Introduction
Spatially and temporally varying soil moisture is being increasingly used as input to hydrological and meteorological models. Knowledge of spatial and temporal variability of field soil helps in characterization of the soil. The use of mathematical model to simulate the water and solute movement into the field soil has accelerated the need to understand the variability of soil properties that affect the interpretation of model output variability.
Soil moisture spatial distribution varies both vertically and laterally due to evapotranspiration and precipitation, influenced by topography, soil texture, and vegetation. While small scale spatial variations are influenced by soil texture, larger scales are influenced by precipitation and evaporation [1]. Field soil encompasses considerable inherent variability in their texture, structure and physical and chemical properties due to variability in parent material and other soil forming factors. Variability in water holding capacity of the soil can adversely affect yield and would complicate irrigation scheduling. Thus,
variability has been found to have significant affect on moisture movement, process and the parameters associated with this process. The characteristics of soil moisture variability is essential for understanding and predicting land surface processes, that varies based on topography, soil texture, and vegetation at different spatial and temporal scales [2]. Thus, the spatial characteristics is a key parameter used in the background statistical error models as well dynamic propagation of the modeled state uncertainty in data assimilation modeling systems [3-7].
Temporal variability of soil water properties are induced by tillage, cropping and other management practices. Surface seal and compaction of soil are predominant phenomenon that affects water flow.
The geostatistical studies for soil moisture variability [8-13] are carried out at the scales of small catchments areas (1-5 km²). S. G. Reynolds [14] found a close relation between sizes on soil moisture variability, with R² of 0.7 considered to be best. To reduce uncertainty, O. R. Dani and R. J. Hanks [15] used state space models for soil water