1. Streamflow Forecasting Project
By:
JD Emmert
Derek Rapp

2. Big Hole River Melrose, Montana

3. OBJECTIVE
The purpose of this project is to identify the large scale climate signals that affect the streamflow of the Big Hole River and to simulate streamflow scenarios given a probabilistic forecast of the climate signals.
Determine the 100-year flood for flood control designs.
Develop models to estimate the monthly streamflow using parametric and nonparametric approaches.

4. High flows occur in the spring months and are primarily driven by snowmelt. Therefore the climate signal comes in the previous winter.

5. Distribution of Monthly Streamflows

6. Climate Signal Indicators
ENSO (El Nino Southern Oscillation)
PDO (Pacific Decadal Oscillation)
PNA (Pacific/North American Pattern Index)
These climate signals were scatterplotted against the streamflows.
The climate signals were also correlated with the streamflows.

7. Flow vs. ENSO Flow vs. PDO Flow vs. PNA

8. The means are shown to be different.
The variances are shown to be similar
The t-test and F-test we performed agree with this.

9. Conditional and Unconditional Probabilities
ENSO categories
High ENSO > 7.0
Low ENSO < -7.0
Neutral ENSO is in between.
Flow categories
Broken into 33rd percentiles.
Probabilistic Forecast
P(high ENSO) = 0.2
P(low ENSO) = 0.5
P(neutral ENSO) = 0.3

10. Theorem of Total Probability
P(high flow) =
P(low flow) =
P(nuetral flow) =
We chose a higher probability for the low ENSO and this results in a higher probability for high flows.

11. Streamflow Scenarios
Historical Value
Given the forecast for climate signals as shown earlier, this is where
the flow for the following spring would be expected to fall.

12. Histogram for Annual Maximum Flow
Gamma distribution is the best fit based on KS test

14. 100 year flood estimates for different methods.
Gamma cms
Log-normal cms
Log-Pearson III cms
Extreme Value I cms
LOCFIT cms

15. Models of Monthly Streamflow
Parametric AR(1) model
Lag-1 Nonparametric K-NN model
Models simulate the months of May and June because they exhibit interesting distributions.
Key statistics are mean, standard deviation, skew, maximum, minimum, and correlation.

16. Mean
Std. Dev.
Skew
Parametric AR(1)
Max
Min
Correlation

17. Lag-1 Nonparametric K-NN
Mean
Std. Dev.
Skew
Lag-1 Nonparametric K-NN
Max
Min
Correlation

18. ANY QUESTIONS ?