The objective of the paper was to assess spatial uncertainty of a soil erodibility factor (K) model resul from the uncertainties in the input parameters (texture and organic matter). The 500 km2 study area was located in central-east Sardinia (Italy) and 152 samples were collected according to pedological horizons. A MonteCarlo analysis was performed incorporating spatial cross-correlation info through joint turning bands simulation was incorporated. A linear coregionalization model was fitted to all direct and cross-variograms of the input variables, which included 3 different structures: a nugget effect, a spherical structure with a shorter range (3500 m) and a spherical structure with a longer range (10000 m). The K factor was then estimated for each set of 500 joint realizations of the input variables, and the ensemble of the model outputs was used to infer the soil erodibility probability distribution function. This approach permits delineation of areas characterised by greater uncertainty, to improve supplementary sampling strategies and K value predictions.

Modelling spatial uncertainty of soil erodibility factor using joint stochastic simulation

Buttafuoco G.
;
Canu A.;
2008

Abstract

The objective of the paper was to assess spatial uncertainty of a soil erodibility factor (K) model resul from the uncertainties in the input parameters (texture and organic matter). The 500 km2 study area was located in central-east Sardinia (Italy) and 152 samples were collected according to pedological horizons. A MonteCarlo analysis was performed incorporating spatial cross-correlation info through joint turning bands simulation was incorporated. A linear coregionalization model was fitted to all direct and cross-variograms of the input variables, which included 3 different structures: a nugget effect, a spherical structure with a shorter range (3500 m) and a spherical structure with a longer range (10000 m). The K factor was then estimated for each set of 500 joint realizations of the input variables, and the ensemble of the model outputs was used to infer the soil erodibility probability distribution function. This approach permits delineation of areas characterised by greater uncertainty, to improve supplementary sampling strategies and K value predictions.
2008
Istituto per la BioEconomia - IBE
Istituto per i Sistemi Agricoli e Forestali del Mediterraneo - ISAFOM
Sardinia
spatial uncertainty
RUSLE
turning band simulation
soil erosion
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/146784
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