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 2024 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 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 2 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.