Skip to main content

The ResSim Blog

This blog was created by Kevin Fagot, a water resources engineer for the modeling of reservoir systems in HEC-ResSim. Programming examples in Python and Jython (scripting language used in HEC-ResSim) are also shown. Since data development and analysis plays an important part in the development and use of this model, you will also find examples of this using Python, Microsoft Excel, and HEC-DSSVue. The posts are labeled by topic so you can use the menu on the right or search by topic.

Search This Blog

Home

Posts

National Flood Insurance Program (NFIP) Maps for an area impacted by Hurricane Katrina

on June 04, 2018

General Reservoir Operations

+

Get link
Facebook
Twitter
Pinterest
Email
Other Apps

National Flood Insurance Program (NFIP) Maps for Goose Creek in Sheridan, Wyoming

on June 04, 2018

General Reservoir Operations

+

Get link
Facebook
Twitter
Pinterest
Email
Other Apps

National Flood Insurance Program (NFIP) Maps for Swamp Creek in Kenmore, Washington

on June 04, 2018

General Reservoir Operations

+

Get link
Facebook
Twitter
Pinterest
Email
Other Apps

National Flood Insurance Program (NFIP) Maps

on June 04, 2018

General Reservoir Operations

+

Get link
Facebook
Twitter
Pinterest
Email
Other Apps

Archive

2022 2
- February 2
  - How do functions in Python work?
  - Computing Kendall's Tau parameter for time series ...

2021 2
- August 1
- July 1
2020 4
- September 2
- August 2
2019 9
- December 1
- September 2
- August 4
- April 1
- March 1
2018 21
- December 2
- September 1
- June 5
- May 11
- March 1
- January 1
2017 34
- November 4
- September 7
- August 2
- July 3
- June 4
- May 4
- April 2
- March 3
- February 2
- January 3
2016 44
- December 5
- November 27
- October 1
- September 1
- August 3
- July 1
- June 2
- May 2
- April 1
- March 1
2015 9
- September 1
- August 1
- June 1
- May 2
- April 2
- March 2

Show more Show less

Labels

Data Analysis
Excel Advanced
Excel Basic
General Reservoir Operations
HEC-DSSVue
HEC-ResSim
Microsoft Access
Python Advanced
Python Basic
Python Intermediate

Report Abuse

Total Pageviews

Popular Posts

Microsoft Excel Basic #2 - Computations on multiple columns of data

In this post, we build on Microsoft Excel Basic #1 by adding additional columns of grades. We copy the average, minimum, and maximum grades for the class and compute the average for each student. In the figure below, we can see that the grades of three tests are now in the spreadsheet. The grades are in columns B, C, and D. To copy the formulas to columns C and D, we highlight cells B8 through B10. We left click on the square in the bottom right hand corner and drag over to column D. Excel automatically updates the formula for columns C and D. From the figure below, we can see that the average in column C has been automatically updated. To compute the average for each student, we are writing a formula in column F. For Bart, the formula is as follows: =AVERAGE(B2:D2) To apply the formula for all students, left click on the square in the bottom right hand corner and drag down to cell F6. Excel will update the formula by applying the incremented row

Python Intermediate #3 - Random Multiplication Practice using randint

In our previous example, we used a list with a for loop in Python to print out multiplication tables. This example allows the user to practice multiplication of two random values. For this post, we will use an if statement along with the randint function from the random module. Additionally we will be using a while loop to ask the user if they want to keep playing. The coding of the randint function can be found at this site: https://docs.python.org/2/library/random.html Scrolling down to randint shows the following: This tells us that this function will produce an random integer from a to b. If we code the following: x = randint(0,9) print("The random integer is ",x) We would expect to get a message giving us a random number between 0 and 9. Instead, we get this: This is because we need to import randint from the random method. We do this as follows: from random import randint x = randint(0,9) print("The random integer is ",x) Now, we

How to Develop a Working Model

The previous blog posts showed specific applications using the HEC-ResSim software. This post will show the creation of an HEC-ResSim model taking the reader through the Watershed Setup module, the Reservoir Network module, and the Simulation module. Note: I have developed a video showing the development of an HEC-ResSim model that can be viewed here: The initial model development will be that of a flow through model. It is recommended that the initial model setup be a flow through model to test the connectivity of the stream network and the overall behavior of the initial model setup. A flow through model consists of the following model characteristics: 1.) No rules of operation are entered into the model 2.) A constant inflow is applied at the upstream ends of the main stem and all tributaries 3.) The reservoirs are set to the top of the conservation pool and are releasing inflow in the lookback period. 4.) The routing is set to null routing. Once the re

Additional error fixing in HEC-ResSim

This post will cover two additional error messages that can occur in the development of an HEC-ResSim model. Special thanks to Logan Osgood-Jacobs of the Seattle District of the Corps of Engineers for supplying these screenshots. Error 1: Ghost Nodes Ghost nodes, or nodes that you did not intend to include in the model, seem to periodically occur in the development of ResSim models. I have gotten these when I have had to edit the watershed configuration and then perform an update to the reservoir network. However, I believe that others have gotten ghost nodes in the initial development of the model. So, how do you tell if you have a ghost node? The most direct way is getting an error message similar to the one below when you compute your simulation. This error message is indicating an issue with Node 81. In the Reservoir Network Module, go to Reports > Advanced > Node List. The report is shown below. There is a node named, "Node 81". Not havi

Computing Kendall's Tau parameter for time series data

Introduction. In this post, the Kendall's Tau parameter will be computed for the annual peak flow data for Dog Branch at St. Paul, Arkansas. This data was derived from the USGS website. The computation of the Kendall's Tau parameter was performed in Microsoft Excel for this analysis. Examining the Kendall's Tau parameter on time series data will help to indicate if a trend exists and how strong it is. If the Kendall's Tau parameter is close to 1, a strong rising trends exists. If the Kendall's Tau parameter is close to -1, a strong falling trend exists. If the parameter is close to zero, then neither a falling or rising trend exists. First Step - Visualize the Data. A good first step in determining a trend is to visually examine the time series data. This is shown below. From the chart above, it appears that a trend is not apparent. In the next steps, the Kendall's Tau parameter will be computed to see if that assumption holds. Next Step - How is the

Powered by Blogger