Woodson Lees Ferry WY flow forecast

Overview

This experimental streamflow forecast procedure was developed by David Woodson as a part of his PhD research at the University of Colorado Boulder. He now produces the forecast as a side project. The forecast uses a machine learning (random forest) model trained on Reclamation annual natural flow for the Colorado River at Lees Ferry, AZ for water years 1921-2024 as the predictand, and the following predictors:

• Pacific Decadal Oscillation (PDO) index: July-August-September average preceding the water year being forecast
• Atlantic Multidecadal Oscillation (AMO) index: July-August-September average preceding the water year being forecast
• Community Earth System Model - Large Ensemble (CESM-LE) forecasts of precipitation and minimum temperature: October through September average coinciding with the water year being forecast

For example, for the 2025 water year forecast (October 2024 - September 2025 total natural flow), the PDO and AMO predictors are the July 2024 through September 2024 averages, and the CESM-LE precipitation and temperature predictors are the forecasted October 2024 through September 2025 averages. The random forest forecast is a 600-member ensemble.

WY 2025 Forecast

Figure 1. Random forest model forecast of Water Year 2024 naturalized streamflow for the Colorado River at Lees Ferry, AZ. (Source: David Woodson)

Figure 2. Two measures of the 'variable importance' (added value) of the four predictors in the random forest forecast model shown in Figure 1. (Source: David Woodson)

Figure 1 shows the naturalized streamflow forecast for Water Year 2025, in green, compared to the historical streamflows, 1921-2024, on which the model is calibrated (black line). The uncertainty in the forecasted 2025 streamflow (i.e., the distribution of the 600 model ensemble members) is depicted in two ways: The green boxplot shows the extent of the interquartile range (25th-75th percentiles) and the median or most-probable forecast (50th percentile), which is 10.2 maf. The green semi-violin density plot shows that within the main bulk of ensemble members below 12.5 maf, there is a peak around 9.0 maf, and a smaller peak around 11.5 maf. The long tail extending to 20 maf indicates there is some potential for a wet year, though less so than for the FY2024 forecast.

Figure 2 shows the 'variable importance' for each predictor over the 1921-2024 training period; each variable does add value to the forecast, and AMO has the highest importance.

WY 2024 Forecast

Figure 3. Random forest model forecast of Water Year 2024 naturalized streamflow for the Colorado River at Lees Ferry, AZ. (Source: David Woodson)

Figure 4. Two measures of the 'variable importance' (added value) of the four predictors in the random forest forecast model shown in Figure 1. (Source: David Woodson)

Figure 3 shows the naturalized streamflow forecast for Water Year 2024, in green, compared to the historical streamflows, 1921-2023, on which the model is calibrated (black line). The uncertainty in the forecasted 2024 streamflow (i.e., the distribution of the 600 model ensemble member) is depicted in two ways: The green boxplot shows the extent of the interquartile range (25th-75th percentiles) and the median or most-probable forecast (50th percentile), which is 10.8 maf. The green semi-violin density plot shows that the forecast has a bimodal distribution with many members clustered around 7 maf, indicating potential for a very dry year, and a smaller second mode around 18 maf, indicating there is some potential for another wet year like WY2023.

Figure 4 shows the 'variable importance' for each predictor over the 1921-2023 training period; each variable does add value to the forecast, and AMO and PDO have the highest importance.