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2003•Journal of Agricultural Engineering (India)Requires access

Stochastic Modelling of Soil Moisture

V. B. Jha, Raj Vir Singh, Sita Ram Bhakar

Open publisher page 3 citations

Abstract

The study was conducted to develop a stochastic model for the soil moisture data of 19 years (1980-1998) for an average depth of 3D-em. The soil moisture series was assumed to be composed of periodic and stochastic component as the trend component was found to be insignificant. The autoregressive stochastic model was fitted to the soil moisture data. By statistical analysis the closeness between generated and historical series was observed. The developed model was then validated by generating data for the last 3 years (1999 -2001) and compared it with observed data for the same years. Mean and standard deviation of the model was found to be very close to observed data, which proves the validity of model.

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What this paper is about

The study was conducted to develop a stochastic model for the soil moisture data of 19 years (1980-1998) for an average depth of 3D-em. The soil moisture series was assumed to be composed of periodic and stochastic component as the trend component was found to be insignificant. The autoregressive stochastic model was fitted to the soil moisture data. By statistical analysis the closeness between generated and historical series was observed. The developed model was then validated by generating data for the last 3 years (1999 -2001) and compared it with observed data for the same years. Mean and standard deviation of the model was found to be very close to observed data, which proves the validity of model.

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Available abstract

The study was conducted to develop a stochastic model for the soil moisture data of 19 years (1980-1998) for an average depth of 3D-em. The soil moisture series was assumed to be composed of periodic and stochastic component as the trend component was found to be insignificant. The autoregressive stochastic model was fitted to the soil moisture data. By statistical analysis the closeness between generated and historical series was observed. The developed model was then validated by generating data for the last 3 years (1999 -2001) and compared it with observed data for the same years. Mean and standard deviation of the model was found to be very close to observed data, which proves the validity of model.

Key concepts: Stochastic modelling, Autoregressive model, Closeness, Standard deviation, Series (stratigraphy), Water content, Mathematics, Statistics

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