Soil hydraulic properties database of the pampas region in Buenos Aires province
Flatland areas, such as the Pampas in South America, rank among the world’s most economically productive landscapes. Over the last century, these regions have been increasingly used for intensive production, which has resulted in significant environmental impacts. These include alterations in pH, s...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Universidad Nacional de Rosario
2025-02-01
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| Series: | Cuadernos del CURIHAM |
| Subjects: | |
| Online Access: | https://cuadernosdelcuriham.unr.edu.ar/index.php/CURIHAM/article/view/261 |
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| Summary: | Flatland areas, such as the Pampas in South America, rank among the world’s most economically productive landscapes. Over the last century, these regions have been increasingly used for intensive production, which has resulted in significant environmental impacts. These include alterations in pH, salinity, and other soil properties; changes in water flows and balances; increased floods and droughts; and heightened wind and water erosion. To address these challenges, numerical process-based models are essential to assess the highly variable, interconnected, and nonlinear processes that drive these impacts. Such models rely on comprehensive soil databases including hydraulic properties to provide representative results. This study aimed to develop a robust database of soil properties for the Buenos Aires Province in Argentina, encompassing much of the Pampas region. Using granulometric and physicochemical data from the Instituto Nacional de Tecnología Agropecuaria (INTA) database, we applied 38 pedotransfer functions to 381 soil profiles to estimate hydraulic parameters. These were compared with seven calibrated parameter sets from the different study sites. This study demonstrated that model performance varies depending on the evaluated function, with specific models excelling in particular variables, highlighting the need for careful selection based on the characteristics of the dataset.
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| ISSN: | 2683-8168 |