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Nutrient balances, river loads, and a counterfactual analysis to determine drivers of Mississippi River nitrogen and phosphorus loads between 1975 and 2017

Published by U.S. Geological Survey | Department of the Interior | Metadata Last Checked: January 27, 2026 | Last Modified: 2021-11-15T00:00:00Z
We explored the possible causes of change in Mississippi River nutrient load trends through an impact evaluation that utilizes counterfactual scenarios to compare observed changes in river loads to changes in river load that might have occurred in the absence of potential causal factors. Prior to the counterfactual analysis, we developed a multiple linear regression model to predict TN and TP load changes over time. We modeled annual FN river loads as a function of current nutrient balances, lagged nutrient balances, and a latent variable representing the aggregate effect of other potential causal factors. We examined two different counterfactual scenarios, using hypothetical inputs to the calibrated TN and TP regression models. For Counterfactual A, the hypothetical inputs were current and lagged nutrient balances held constant at 1975 levels through 2017, and the Year terms were the same as the original inputs. The objective of holding the nutrient balance inputs constant was to investigate how river nutrient loads might have changed between 1975 and 2017 in the absence of any variability in nutrient balances after 1975. For Counterfactual B, the hypothetical inputs were the latent Year term held constant at 1975 levels through 2017, and the current and lagged nutrient balance inputs were the same as in the original inputs. The objective of holding the Year input constant at 1975 was to investigate how river nutrient loads might have changed between 1975 and 2017 in the absence of any variability in latent processes, potentially including BMP implementation, watershed buffering capacity, and other factors. The impact analysis compared the mean annual counterfactual analysis results to the mean original regression results for the time period 2013 to 2017. The original regression results refer to the predicted river loads estimated from the calibrated regression model using the original data.

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