Streamflow data is used throughout the world in applications such as flooding, agriculture, and urban planning. Understanding daily and seasonal patterns in streamflow is important for decision makers, so that they can accurately predict and react to seasonal changes in streamflow for the region. This understanding of daily and seasonal patterns has historically been achieved through interpretation of observed historical data at stream reaches throughout the individual regions. Developing countries have limited and sporadic observed stream and rain gage data, making it difficult for stakeholders to manage their water resources to their fullest potential. In areas where observed historical data is not readily available, the European Reanalysis Interim (ERA-Interim) data provided by the European Center for Medium-Range Weather Forecasts (ECMWF) can be used as a surrogate. The ERA-Interim data can be compared to historic observed flow to determine the accuracy of the ERA-Interim data using statistical measures such as the correlation coefficient, the mean difference, the root mean square error, R2 coefficients and spectral angle metrics. These different statistical measures determine different aspects of the predicted data's accuracy. These metrics measure correlation, errors in magnitude, errors in timing, and errors in shape. This thesis presents a suite of tests that can be used to determine the accuracy and correlation of the ERA-Interim data compared to the observed data, the accuracy of the ERA-Interim data in capturing the overall events, and the accuracy of the data in capturing the magnitude of events. From these tests, and the cases presented in this thesis, we can conclude that the ERA-Interim is a sufficient model for simulating historic data on a global scale. It is able to capture the seasonality of the historical data, the magnitude of the events, and the overall timing of the events sufficiently to be used as a surrogate dataset. The suite of tests can also be applied to other applications, to make comparing two datasets of flow data a quicker and easier process.



College and Department

Ira A. Fulton College of Engineering and Technology; Civil and Environmental Engineering



Date Submitted


Document Type





correlation, streamflow modelling, statistical analysis, ERA-Interim