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Introduction

This report details the calibration of the GR4J hydrological model using streamflow (Q) data. The calibration process is conducted through both web-based and local R-based environments. The GR4J model is an essential tool for understanding catchment behavior, and its calibration is crucial for accurate predictions.

Data and Methodology

Data on streamflow is used in the calibration process, which is a crucial component of hydrological modeling. From [Start Date] to [End Date], the data is available.

GR4J Model: For modelling streamflow in catchments, hydrologists often turn to the well-known GR4J model. It has a number of parameters that need to be calibrated in order to produce reliable predictions.

Web-Based Model Calibration

  • Using the ShinyGR software, web-based model calibration is the first step in the calibration procedure. The following stages are included in this section:
  • Data Preparation: Inputdata is used to convert dates and prepare the data for calibration.
  • Web-based model calibration is done using the ShinyGR tool, and the simulation period is from January 1, 2015, to December 31, 2018.
  • Local R-Based Model Calibration
  • In the nearby R-based environment, the calibration procedure is still ongoing. The following stages are included in this section:
  • Calibration Without modification: Using the CalGR function, the model is calibrated without data modification. The calibration criteria is the Nash-Sutcliffe Efficiency (NSE).
  • Using the calibrated parameters, the model is run, and the results are shown to show how well the simulated and observed streamflow match up.
  • Extraction of Results: For analysis, the calibration parameters and the simulated streamflow (Q) are extracted.
  • Model Calibration With Transformation: The CalGR function is used to calibrate the model with data transformation. The calibrating criteria is Kling-Gupta Efficiency (KGE).
  • Similar to the previous stage, simulation and plotting (with transformation) involve simulating the model with modified data and visualising the results.

Results and Analysis

  • Results of Calibration: The calibrated model parameters' values as well as the final NSE or KGE value of the calibration objective function are shown.
  • Comparison of Calibration Results: The calibration results obtained with and without data modification are compared.
  • Figures and Tables
  • Flow time series:

References

R Core Team. (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Retrieved from https://www.R-project.org/

Wickham, H., & Grolemund, G. (2017). R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O'Reilly Media.

Roger D. Peng. (2016). R Programming for Data Science. Leanpub. Retrieved from https://leanpub.com/rprogramming

Hadley Wickham. (2019). Advanced R. CRC Press.

Garrett Grolemund, Hadley Wickham. (2017). R Graphics Cookbook: Practical Recipes for Visualizing Data. O'Reilly Media.

J.J. Allaire. (2019). RStudio: Integrated Development for R. RStudio, PBC. Retrieved from https://www.rstudio.com/

Phil Spector. (2008). Data Manipulation with R. Springer.

Norman Matloff. (2011). The Art of R Programming: A Tour of Statistical Software Design. No Starch Press.

Hans Peter Luhn. (2015). The History of Statistical Programming Languages: From FORTRAN to R. Springer.

John M. Chambers. (2008). Software for Data Analysis: Programming with R. Springer.

Graham Williams. (2010). Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery. Springer.

Philipp K. Janert. (2010). Data Analysis with Open Source Tools: A Hands-On Guide for Programmers and Data Scientists. O'Reilly Media.

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