Geospatial Analysis of Transmissivity and Uncertainty in a Semi-Arid Karst Region
Aquifer properties, such as hydraulic transmissivity <i>T</i> and its spatial variability, are fundamental for sustainable groundwater exploitation in arid regions. Especially in karst aquifers, spatial variability can be considerable, and the application of geostatistical methods allows...
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MDPI AG
2024-03-01
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author | Thiago dos Santos Gonçalves Harald Klammler Luíz Rogério Bastos Leal |
author_facet | Thiago dos Santos Gonçalves Harald Klammler Luíz Rogério Bastos Leal |
author_sort | Thiago dos Santos Gonçalves |
collection | DOAJ |
description | Aquifer properties, such as hydraulic transmissivity <i>T</i> and its spatial variability, are fundamental for sustainable groundwater exploitation in arid regions. Especially in karst aquifers, spatial variability can be considerable, and the application of geostatistical methods allows for spatial interpolation and mapping based on observations combined with the quantification of uncertainties. Moreover, direct measurements of <i>T</i> are typically scarce, while those of specific capacity <i>S<sub>c</sub></i> are more frequent. In this study, we establish the linear regression relationship between the logarithms of <i>T</i> and <i>S<sub>c</sub></i> measured in 51 wells in a semi-arid karst region in Northeastern Brazil. This relationship is used to estimate empirical values log<i>T<sub>emp</sub></i> based on measurements of log<i>S<sub>c</sub></i> at 269 wells. Log<i>T<sub>emp</sub></i> values are found to be normally distributed with an isotropic variogram of a significant nugget effect (attributed to local-scale karst features) and approximately 10 km range (attributed to larger-scale gradual changes in karst feature density). Ordinary kriging cross-validation indicates an optimum number of 25 neighboring wells for interpolation, which is used in a conditional sequential Gaussian simulation (SGSIM) to generate 500 realizations of log<i>T<sub>emp</sub></i> with respective maps of standard deviations and probabilities of (not) exceeding threshold values. High-transmissivity areas mostly coincide with karstified river valleys, while low-transmissivity areas occur toward the edges where aquifer thickness decreases. The resulting transmissivity maps are relevant for optimizing regional water management strategies, which includes stochastic approaches where transmissivity realizations can be used to parameterize multiple runs of numerical groundwater models. |
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spelling | doaj.art-c2e88d040ced47f18e82175317417e222024-03-12T16:58:03ZengMDPI AGWater2073-44412024-03-0116578010.3390/w16050780Geospatial Analysis of Transmissivity and Uncertainty in a Semi-Arid Karst RegionThiago dos Santos Gonçalves0Harald Klammler1Luíz Rogério Bastos Leal2Institute of Geosciences, Federal University of Bahia, Salvador 40170, BrazilInstitute of Geosciences, Federal University of Bahia, Salvador 40170, BrazilInstitute of Geosciences, Federal University of Bahia, Salvador 40170, BrazilAquifer properties, such as hydraulic transmissivity <i>T</i> and its spatial variability, are fundamental for sustainable groundwater exploitation in arid regions. Especially in karst aquifers, spatial variability can be considerable, and the application of geostatistical methods allows for spatial interpolation and mapping based on observations combined with the quantification of uncertainties. Moreover, direct measurements of <i>T</i> are typically scarce, while those of specific capacity <i>S<sub>c</sub></i> are more frequent. In this study, we establish the linear regression relationship between the logarithms of <i>T</i> and <i>S<sub>c</sub></i> measured in 51 wells in a semi-arid karst region in Northeastern Brazil. This relationship is used to estimate empirical values log<i>T<sub>emp</sub></i> based on measurements of log<i>S<sub>c</sub></i> at 269 wells. Log<i>T<sub>emp</sub></i> values are found to be normally distributed with an isotropic variogram of a significant nugget effect (attributed to local-scale karst features) and approximately 10 km range (attributed to larger-scale gradual changes in karst feature density). Ordinary kriging cross-validation indicates an optimum number of 25 neighboring wells for interpolation, which is used in a conditional sequential Gaussian simulation (SGSIM) to generate 500 realizations of log<i>T<sub>emp</sub></i> with respective maps of standard deviations and probabilities of (not) exceeding threshold values. High-transmissivity areas mostly coincide with karstified river valleys, while low-transmissivity areas occur toward the edges where aquifer thickness decreases. The resulting transmissivity maps are relevant for optimizing regional water management strategies, which includes stochastic approaches where transmissivity realizations can be used to parameterize multiple runs of numerical groundwater models.https://www.mdpi.com/2073-4441/16/5/780groundwateraquiferspecific capacityvariogramkrigingstochastic simulation |
spellingShingle | Thiago dos Santos Gonçalves Harald Klammler Luíz Rogério Bastos Leal Geospatial Analysis of Transmissivity and Uncertainty in a Semi-Arid Karst Region Water groundwater aquifer specific capacity variogram kriging stochastic simulation |
title | Geospatial Analysis of Transmissivity and Uncertainty in a Semi-Arid Karst Region |
title_full | Geospatial Analysis of Transmissivity and Uncertainty in a Semi-Arid Karst Region |
title_fullStr | Geospatial Analysis of Transmissivity and Uncertainty in a Semi-Arid Karst Region |
title_full_unstemmed | Geospatial Analysis of Transmissivity and Uncertainty in a Semi-Arid Karst Region |
title_short | Geospatial Analysis of Transmissivity and Uncertainty in a Semi-Arid Karst Region |
title_sort | geospatial analysis of transmissivity and uncertainty in a semi arid karst region |
topic | groundwater aquifer specific capacity variogram kriging stochastic simulation |
url | https://www.mdpi.com/2073-4441/16/5/780 |
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