Geostatistical Simulation in Assessing Spatial Uncertainties of Soil Physical Vulnerability at the Watershed Scale

作     者：Nunes, Rômulo Félix Costa, Angelo Garcia Dutra, Leonir Aldrighi Fuentes-Guevara, Miguel David Rodrigues, Aryane Araujo Soares, Maurício Fornalski Siqueira, Tirzah Moreira de Lima, Cláudia Liane Rodrigues 

作者机构：Soil and Water Management and Conservation Post-Graduate Program Faculty of Agronomy Federal University of Pelotas Campus Universitário s/n Rio Grande do Sul Capão do LeãoCEP: 96010-900 Brazil Araucária Fundation - Unicentro Agronomy Graduate Program Cedeteg Campus PR GuarapuavaCEP: 85040-167 Brazil Water Resources Post-Graduate Program Center of Technological Development Federal University of Pelotas Campus Porto Rua Gomes Carneiro n. 01 Rio Grande do Sul PelotasCEP: 96010-610 Brazil Crops Team – Consulting Research and Development Rio Grande do Sul Santa Maria Brazil Engineering Center Federal University of Pelotas Rio Grande do Sul Pelotas Brazil Department of Soil Faculty of Agronomy Federal University of Pelotas Campus Universitário s/n Rio Grande do Sul Capão do LeãoCEP: 96010-900 Brazil 

出 版 物：《SSRN》 

年 卷 期：2023年

核心收录：

主　　题：Soils 

摘      要：Evaluating the physical quality of soil (SPQ) at the watershed scale is crucial for improved management and conservation of soil and water within the watershed. This study aims to assess the spatial uncertainties of soil physical vulnerability (SPV), based on the characterization of the distribution and spatial variability of physical-hydric attributes (PHA), using geostatistical simulation within the established sample grid in the Santa Rita watershed (SRW), located in Pelotas, Rio Grande do Sul, Southern Brazil. Variability and spatial distribution were assessed using the Matheron estimator and the sequential Gaussian simulation (SGS) method, respectively. Uncertainties of macroporosity (Ma), bulk density (Bd), and saturated hydraulic conductivity (Ks) were evaluated based on cumulative frequency distribution curves and coefficient of variation mapping from simulations. The SPV was assessed by integrating different critical zone maps based on Ma, Bd, and Ks. Among the studied attributes, Ks exhibited the highest heterogeneity. SGS effectively characterized the variability and spatial distribution of Ma, Bd, and Ks, identifying critical areas and developing maps highlighting zones with compromised soil quality. Ks required more simulated random fields, while Ma presented fewer uncertainties. The spatial uncertainties for SPV were minimal, despite using different combinations of PHA in the assessment. Soils under native forest showed a lower tendency for physical degradation, whereas soils under crops and native field in the southern portion of the SRW exhibited a higher tendency for physical degradation. Mapping SPV is crucial for assessing SPQ, identifying areas prone to fragility, and promoting sustainable soil and water management. © 2023, The Authors. All rights reserved.