Modelling spatial uncertainty of soil erodibility factor using joint stochastic simulation

Abstract

Soil erosion varies greatly over space and is commonly estimated using the revised universal soil loss equation (RUSLE). Neglecting information about estimation uncertainty, however, may lead to improper decision-making. One geostatistical approach to spatial analysis is joint stochastic simulation, which draws alternative, equally probable, joint realizations of a regionalized variable. Differences between the realizations provide a measure of spatial uncertainty and allow us to carry out an error propagation analysis.The objective of this paper was to assess spatial uncertainty of a soil erodibility factor (K) model resulting from the uncertainties in the input parameters (texture and organic matter). The 500 km 2 study area was located in central-eastern Sardinia (Italy) and 152 samples were collected. A Monte Carlo analysis was performed where spatial cross-correlation information 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 three different structures: a nugget effect, a spherical structure with a shorter range (3500 m) and a spherical structure with a longer range (10 000 m). The K factor was then estimated for each set of the 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 permitted delineation of the areas characterized by greater uncertainty, to improve supplementary sampling strategies and K value predictions.

Spatial Modeling of Soil Erosion Risk and Its Implication for Conservation Planning: the Case of the Gobele Watershed, East Hararghe Zone, Ethiopia

“…On the contrary, Wang et al [92] found improvement in the state of erosion risk in the Danjiangkou reservoir area, China, where the eroded areas have declined from 32.1% in 2004 to 25.43% in the 2010 study period. Moreover, Jiang et al [16] reported that the eroded area has Therefore, since there is geospatial variation in soil erosion risk distribution across the watershed area, identification of priority area is the key factor for planning and implementing appropriate SWC [10][11][12][13][14][15][16][17][18][19]51,90,142]. Accordingly, we prioritized areas with a higher and increasing soil loss rate as SWC priority areas (Table 6).…”

Accuracy and uncertainty assessment on geostatistical simulation of soil salinity in a coastal farmland using auxiliary variable

“…These approaches are regarded as "probabilistic" because they explicitly recognize the uncertainty associated with the prediction and assess such uncertainty by the analysis on a set of stochastic simulations (Castrignanò and Buttafuoco 2004;He et al 2010). The statistical quantities, as derived from a series of equally probable realizations, allow uncertainty to be assessed and then the consequences of data uncertainty on decision making evaluated (Castrignanò et al 2008;Barca and Passarella 2008). The last decade has witnessed an increasing awareness of the importance of assessing the uncertainty about the value of soil properties at unsampled locations (Grunwald et al 2004;Diodato 2005).…”

Comparison of statistical prediction methods for characterizing the spatial variability of apparent electrical conductivity in coastal salt-affected farmland

“…Kriging-based estimators are exact interpolators that provide best, unbiased, linear estimation of the variables by minimizing the estimation variance. With respect to the geostatistical simulation methods, which consider global statistics more than local accuracy, have been developed largely in direct response to the inadequate measures of spatial uncertainty or finite data (Goovaerts 1999;Castrignanò et al 2008). In this study, apparent electrical conductivity was measured in a fairly quick manner by the mobile EMI system and geostatistical simulation methods were not considered.…”