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How to convert daily streamflow data into monthly, yearly streamflow data Using python

Hydrological Data Analysis: 001

Hydrological data analysis often involves working with time series data. In hydrology, streamflow is a critical parameter that is monitored and analyzed regularly. Streamflow data is usually recorded daily, but for many applications, it is useful to have the data aggregated into monthly or yearly values. In this blog post, we will explore how to convert daily streamflow data into monthly and yearly values using Python.


Importing the Required Libraries

Before we start working on the data, we need to import the necessary libraries. We will be using the Pandas library for data manipulation and the Matplotlib library for visualization. We can import these libraries using the following code


Loading the Data

The first step is to load the daily streamflow data into a Pandas data frame. We assume that the data is stored in a CSV file named "streamflow_data.csv" and that the data has two columns: "Date" and "Streamflow". We can use the Pandas read_csv() function to load the data into a data frame:




Converting Daily Data into Monthly Data

To convert the daily streamflow data into monthly data, we need to group the data by month and then calculate the average streamflow for each month. We can use the groupby() and resample() functions of Pandas to achieve this:




The resample() function is used to group the data by month (using the 'M' argument), and the mean() function is used to calculate the average streamflow for each month. The resulting data frame, monthly_data, will have one row for each month, with the average streamflow for that month.

Converting Daily Data into Yearly Data

To convert the daily streamflow data into yearly data, we need to group the data by year and then calculate the total streamflow for each year. We can use the groupby() and resample() functions of Pandas to achieve this:





The resample() function is used to group the data by year (using the 'Y' argument), and the sum() function is used to calculate the total streamflow for each year. The resulting dataframe, yearly_data, will have one row for each year, with the total streamflow for that year.

Visualizing the Data

We can visualize the monthly and yearly streamflow data using matplotlib. For example, to plot a line graph of the monthly streamflow data, we can use the following code




Similarly, to plot a bar graph of the yearly streamflow data, we can use the following code:





Conclusion

In this blog post, we have explored how to convert daily streamflow data into monthly and yearly data using Python. We have used the Pandas library to group the data by month and year and to calculate the average and total streamflow, respectively. We have also used the matplotlib library to visualize the data in the form of line and bar graphs.

Converting daily streamflow data into monthly and yearly data is an essential step in many hydrological applications. It can help us to identify long-term trends and patterns in the data, which can be useful for water resource management and planning.

With Python and the Pandas library, the process of converting daily data into monthly and yearly data is straightforward and can be achieved in just a few lines of code. This allows hydrologists and water resource managers to process large datasets quickly and efficiently, making it easier to extract valuable insights from the data.

GitHub: Code  

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