Spatio-temporal Dynamics of a Soil Moisture Field: Sampling Error Analysis with Simulation Study
Spatio-temporal Dynamics of a Soil Moisture Field: Sampling Error Analysis with Simulation Study
In this study, effects of intermittent visit of observation satellite, partial coverage of remote sensing, heterogeneity of soil properties and precipitation on soil moisture estimations were investigated to develop a sampling strategy. In the soil moisture sampling error analysis, a modified form of theoretical soil moisture model proposed by [1], the WGR model proposed by [2] for use of generating rainfall and the Turning Bands Method for use of generating two dimensional random fields were employed. The evaluation of study results indicates that the sampling error is mainly dominated by sampling interval. The effect of heterogeneity of soil properties and rainfall on sampling error is considerably smaller than that of intermittent visit of observation satellite. The study results suggest that the sampling error generated by other factors such as heterogeneity of rainfall and soil properties, topography and climate conditions can be significantly reduced by increasing the sampling interval, for example, at least twice per day. The effect of partial coverage on sampling error can be ignored provided that the annual mean of coverage portion is higher than 90 %. The impact of water retention capacity of fields on the sampling error seems to be significant. More specifically, the smaller the water retention capacity of fields (i.e., a smaller soil porosity and a thinner active soil depth) the larger the sampling error.
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- [1] D. Entekhabi and I. Rodriguez-Iturbe, “Analytical framework for the
characterization of the space-time variability of soil moisture”. Advances
in Water Resources, 17, 25-45, 1994.
[2] E. Waymire, V.K. Gupta and I. Rodriguez-Iturbe, “Spectral theory of
rainfall intensity at the Meso-b scale”, Water Resources Research,
20(10), 1453-1465, 1984.
[3] D. Entekhabi and P.S. Eagleson, “Land surface hydrology
parametrization for atmospheric general circulation models including
subgrid scale spatial variability”, J. Climate, 2, 816-831, 1989.
[4] G. Kim and A.P. Barros, A.P., “Space-time characterization of soil
moisture from passive microwave remotely sensed imagery and ancillary
data”, Remote Sensing of Environment, 81, 393-403, 2002.
[5] K.L. Brubaker and D. Entekhabi, “An analytic approach to modeling
land-atmosphere interaction 1. Construct and equilibrium behavior”,
Water Resources Research, 31(3), 619-632, 1995.
[6] I. Rodriguez-Iturbe, D. Entekhabi and R.L. Bras, “Non-linear
dynamics of soil moisture at climate scales 1. Stochastic analysis”, Water
Resources Research, 27(8) 1899-1906, 1991a.
[7] I. Rodriguez-Iturbe, D. Entekhabi, J.S. Lee and R.L. Bras,”Non-linear
dynamics of soil moisture at climate scales 2. Chaotic analysis”, Water
Resources Research, 27(8), 1907-1915, 1991b.
[8] P.S. Eagleson, “Climate, soil and vegetation 3. A simplified model for
soil moisture movement in the liquid phase”, Water Resources Research,
14(5), 722-730, 1978.
[9] C.P. Kim and J.N.M. Stricker, “Influence of spatially variable soil
hydraulic properties and rainfall intensity on the water budget”, Water
Resources Research, 32(6), 1699-1712, 1996.
[10] C.E. Graves, J.B. Valdés, S.S.P. Shen and G.R. North, “Evaluation
of sampling error of precipitation from space and ground sensors”,
Journal of Applied Meteorology, 32, 374-384, 1993.
[11] G.A. Meehl and W.M. Washington, “A comparison of soil moisture
sensitivity in two climate models”, Journal of Atmospheric Science, 45,
1476-1492, 1988.
[12] P. Whittle, “Topographic correlation, power-law covariance
functions, and diffusion”, Biometrica, 49(3), 305-314, 1962.
[13] S. Islam, R.L. Bras and I. Rodriguez-Iturbe,” Multi-dimensional
modeling of cumulative rainfall: parameter estimation and model
adequacy through a continuum of scales”, Water Resources Research,
24, 992-995, 1988.
[14] J.B. Valdés, S. Nakamoto, S.S.P. Shen and G.R. North, “Estimation
of multi-dimensional precipitation parameters by Arial estimates of
oceanic rainfall”, Journal of Geophysical Research (Atmos.), 95(D3),
2101-2111, 1990.
[15] R.W. Keopsell and J.B. Valdés, “Multi-dimensional rainfall
parameter estimation from sparse network”, ASCE Journal of Hydraulic
Engineering, 117, 832-850, 1991.
[16] G. Matheron, “Intrinsic random functions and their applications”,
Advances in Applied Probability, 5, 439-448, 1973.
[17] A. Mantoglou and J.L. Wilson, “The turning bands method for
simulation of random fields using line generation by a spectral method”,
Water Resources Research, 18(5), 1379-1394, 1982