CEE 6440 GIS in Water Resources

Fall 2010

Naho Orita

Peak Flow Rate Prediction in Logan River

by Different Interpolations of SNOTEL Data

Objective

The objective of this project is to determine the most suitable way to predict the peak streamflow in Logan River, UT using SNOTEL data.

Background Information

Even though a small percentage of precipitation within the US falls as snow, it could be a dominant source of streamflow in some areas, including Logan, UT. Logan River is a tributary of Little Bear River in Utah. The Logan River Watershed lies on North East corner of Utah beyond the Utah/Idaho border as shown in Figure 1 below.

Figure 1. Logan River Basin and Topography

The Logan River is mostly recreational water above the state first dam and is used irrigate farm land downstream from the dam.

Every spring time the Logan River is heavily impacted by the snow melt from the surrounding mountains. The average streamflow of Logan River is usually between 100 to 150 cfs where the spring peak flow can be up to 1500 cfs.

It is important to predict the peak streamflow in Logan River for recreational safety and farm planning. Usually, the peak streamflow is predicted using Snow Water Equivalent (SWE) on a set date (e.g. Mar. 1^st or Apr. 1^st) (Personal Communication Dr. Tarboton.) The Purpose of this study is to evaluate the peak streamflow prediction using a set date SWE, maximum SWE of the water year, and the Total precipitation of the year up to the set date.

Data Acquisition

The watershed boundaries and flowlines were observed from National Hydrography Dataset Plus (NHDPlus.) The Great Basin is hydrologic region 16, the Logan River watershed is found in unit b. The daily precipitation and Snow Water Equivalent (SWE) were obtained from SNOTEL site in Natural Resources Conservation Service (NRCS) website. There are six active SNOTEL stations in Logan Canyon area as shown in Figure 2 below, however, four of them shown as green dots were installed recently so that they do not have sufficient data for my project.

Figure 2. SNOTEL stations

Thus, the data from two sites; 823 (Tony Grove Lake) and 1013 (Temple Fork) shown as the red dots above dated from water year (Nov. 1^st of previous year to Sep. 30^th) of 2002 to 2009 were chosen to be used in this project.

The daily discharge data were obtained from USGS website, station number 10109000 which is located in the Logan River dated from 1999 to 2009 (Figure 3.)

Figure 3. Daily Average Discharge for Logan River

The peak streamflow from each year was manually found in the dataset and entered in Excel worksheet for statistics analysis.

Statistics

First, the March 1^st SWE were plotted against the peak streamflow as below followed by the April 1^st SWE. (Figure 4, 5)

Figure 4. March 1st vs. Peak Stream Flow

Figure 5. April 1st vs. Peak Stream Flow

As can be seen, there are stronger correlation with the Tony Grove Lake SNOTEL data to the peak streamflow rate. Then, the maximum SWE of the year was plotted against the peak streamflow rate as below.

Figure 6. Max. SWE vs Peak StreamFlow

Again, Tony Grove Lake has stronger correlation with the peak streamflow.

Among the three trial of evaluation, the highest correlation found was the april 1^st SWE data and the peak streamflow.

Next, total precipitation of the water year up to the date of prediction was determined. (Figure 7, 8)

Figure 7. Total Precipitation on March 1st vs. the Peak StreamFlow

Figure 8. Total Precipitation on April 1st vs. the peak StreamFlow

As shown in Figure 8, the highest correlation was found between the total precipitation on April 1^st and the peak streamflow for both Temple Fork Data as well as Tony Grove Lake Data.

Conclusions

Based on its highest correlation, it can be stated that the total precipitation on April 1^st is the most suitable way to predict the peak streamflow in Logan River, UT using SNOTEL data. The peak streamflow can be explained by the equation below.

Peak Streamflow (cfs)=99.088*Total Precip@TempleForkSNOTELonApr.1st(in)-1075.5

Recommendation

For future work, I would personally recommend to have a prediction model for when the peak streamflow will occur.