Visualization and analysis of streamflow data
1.Host Institution:
Jomo Kenyatta University of Agriculture and Technology (JKUAT), P.O. Box 62 000 – 00200 Nairobi, Kenya
2. Supervisors:
(1) John M. Gathenya, PhD (mgathenya@gmail.com)
Professor, Hydrology, Climate Services, Environmental Services, Jomo Kenyatta University of Agriculture & Technology, School of Biosystems and Environmental Engineering
(2) Roger Stern (r.d.stern@reading.ac.uk), Professor of Applied Statistics (Emeritus), University of Reading, UK
3. Project Description:
The objective of this project is to develop R-instat menus for visualization, quality control and statistical analysis of streamflow data.
The visualization includes tools to do the following:
- Data availability plots (showing start, end and gaps in time series)
- Cleaning and screening daily streamflow data;
- Calculate and visualize various summary statistics and metrics;
- Filtering of and handling dates, customizing data and metrics,
- Flow duration curves
- Mass curves to check consistency of streamflow data
The analysis includes:
- Frequency analysis of peak flows
- Frequency analysis of low flows
- Time series analysis for trends, break-points
- Separation of streamflow into base flow and surface flow and calculation of base flow index Calculation of mass recession curves.
4. Expected outcomes:
Many countries combine their climatic and hydrological services. The aims of this internship project is to develop and exploit resources for the processing of hydrological data that parallel those for climatic variables. These combine the development of standard methods of analysis for streamflow data with the use of complex simulation models.
Applications for this internship should be submitted via the online application system, stating clearly the title of the internship.
Deadline for applications: February 28th, 2021 at 11:59 PM Central Africa Time (CAT)
Any inquiries about these internships should be sent to:
aims-ms4cr.internship@nexteinstein.org.