Using hydrologic patterns and precipitation data to construct an empirical model for understanding cumulative saturation
Date of Original Version
Soil morphology is often used as a predictor of water table levels in hydric soil determinations or other on-site evaluations. These predictions are based on the assumption that the relationships between water table levels and morphologic properties were established during typical weather patterns. Because climatic factors such as precipitation and evapotranspiration vary widely from year to year, there are often concerns about the validity of using short-term hydrologic data to predict longer term relationships. In this study, we monitored water table levels in three soils formed in glacial fluvial and till parent materials for 18 mo. Using these data, we developed a simple model that applies archived precipitation data and established hydrologic patterns to predict water table levels and cumulative saturation. The model was calibrated for each site through an iterative process. The minimal average differences between actual and predicted water table levels (<12 cm), R 2 values (0.68-0.86), and the similarities between the cumulative saturation curves compiled from predicted and measured data suggested that the model performed well at predicting water table depths. Archived precipitation data (1950-1984) were applied to predict longer term cumulative saturation, cumulative saturation during a period of excessive wetness, and cumulative saturation associated with a period of moderate to extreme drought. The model showed that cumulative saturation can vary widely between years depending on climatic conditions. This variability in cumulative saturation should be considered when making land use decisions based on the identification of hydromorphic features such as redox depletions and concentrations. © Soil Science Society of America.
Publication Title, e.g., Journal
Soil Science Society of America Journal
Morgan, Charles P., and Mark H. Stolt. "Using hydrologic patterns and precipitation data to construct an empirical model for understanding cumulative saturation." Soil Science Society of America Journal 73, 2 (2009). doi: 10.2136/sssaj2007.0358.