Spatial and Temporal Variability of Soil Moisture

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

Retrieval of Both Soil Moisture and Texture Using TerraSAR-X Images

“…In the case of semi-arid and arid regions, this parameter is particularly important for water resources and irrigation management decisions, understanding land surface processes, and estimating runoff and soil erosion potential [1]. Spatio-temporal soil moisture information is increasingly used as input or a means of validation for multi-hydrological water balance models [2]. The spatial distribution of water in the soil varies both vertically and laterally, as a consequence of variations in precipitation and evapotranspiration, and the influences of topography, soil texture, and vegetation.…”

Geostatistical analysis of soil moisture distribution in a part of Solani River catchment

“…Downer and Ogden (2003) estimated soil moisture using gridded surface subsurface hydrologic analysis (GSSHA) hydrologic model with a root mean square error of 0.1. Pandey and Pandey (2010) mapped soil moisture in Udaipur, India, applying ordinary kriging. The study concluded that the krigged values were consistent and true representative of soil moisture values.…”

Analysis of spatial variability of near-surface soilmoisture to increase rainfall-runoff modellingaccuracy in SW Hungary

“…Spatially and temporally varying soil moisture has been increasingly used as an input parameter to hydrological and meteorological models as soil moisture plays a crucial role in the surface runoff of water originating from heavy rainfalls [1]. The spatial distribution of soil moisture varies as a function of the spatial distribution of evapotranspiration and precipitation, ultimately influ-enced by topography, soil texture, and land use type [5][6][7][8]. Alternatives to collecting sufficient knowledge on its spatial distribution include high resolution in situ measurement and monitoring [17,18] or satellite remote sensing applications [19,20].…”

“…One way to estimate the magnitude of runoff and peak discharge values is the use of rainfall-runoff numeric models [5,6]. However, such models require accurate spatial parameterization of antecedent catchment states [6,7].…”

“…One way to estimate the magnitude of runoff and peak discharge values is the use of rainfall-runoff numeric models [5,6]. However, such models require accurate spatial parameterization of antecedent catchment states [6,7]. One crucial variable that contributes to the overall runoff/infiltration ratio during a rainfall event is soil moisture [8][9][10][11][12][13].…”