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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.

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Storing Variables in a Single State Variable

on January 02, 2018

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Examination of Minimum and Specified Release Rules in HEC-ResSim

on November 28, 2017

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Examination of Multiple Specified Release Rules in HEC-ResSim

on November 27, 2017

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Examination of Specified and Maximum Release Rules in HEC-ResSim

on November 27, 2017

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Available Time Steps in HEC-ResSim

on November 26, 2017

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Developing Simulations in HEC-ResSim

on September 20, 2017

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Savannah River System Reservoirs during Hurricane Irma

on September 15, 2017

General Reservoir Operations

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Apalachicola-Chattahoochee-Flint (ACF) System Reservoirs during Hurricane Irma

on September 15, 2017

General Reservoir Operations

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Rainfall gage readings from Hurricane Harvey

on September 12, 2017

General Reservoir Operations

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  - How do functions in Python work?
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Microsoft Excel Basic #2 - Computations on multiple columns of data

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How to Develop a Working Model

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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

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

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