hydroeval 0.0.3

HydroEval: An Efficient Evaluator for Streamflow Time Series In Python

HydroEval - An efficient evaluator for streamflow time series in Python

HydroEval is an open-source evaluator for streamflow time series in Python. It is licensed under GNU GPL-3.0 (see licence file provided). The purpose of this evaluator is to compare observed and simulated hydrographs using one or more objective functions. HydroEval is designed to calculate all objective functions in a vectorised manner (using numpy, and therefore C code in the background) which makes for very efficient computation of the objective functions.

How to Install

HydroEval is available on PyPI, so you can simply use pip and the name of the package:

python -m pip install hydroeval

You can also use pip and a link to the GitHub repository directly:

python -m pip install git+https://github.com/ThibHlln/hydroeval.git

Alternatively, you can download the source code (i.e. the GitHub repository) and, from the downloaded directory itself, run the command:

python setup.py install

How to Use

A tutorial in the form of a Jupyter notebook is available to get started with the usage of HydroEval's API. The input files required for the tutorial are all provided in the examples/ folder.

How to Cite

If you are using HydroEval, please consider citing the software as follows (click on the link to get the DOI of a specific version):

• Hallouin, T. (XXXX). HydroEval: Streamflow Simulations Evaluator (Version X.X.X). Zenodo. https://doi.org/10.5281/zenodo.2591217

Objective Functions Available

The objective functions currently available in HydroEval to evaluate the fit between observed and simulated stream flow time series are as follows:

• Nash-Sutcliffe Efficiency (nse)
• Original Kling-Gupta Efficiency (kge) and its three components (r, α, β)
• Modified Kling-Gupta Efficiency (kgeprime) and its three components (r, γ, β)
• Non-Parametric Kling-Gupta Efficiency (kgenp) and its three components (r, α, β)
• Root Mean Square Error (rmse)
• Mean Absolute Relative Error (mare)
• Percent Bias (pbias)

Moreover, KGE and NSE can be calculated in a bounded version following Mathevet et al. (2006):

• Bounded Nash-Sutcliffe Efficiency (nse_c2m)
• Bounded Original Kling-Gupta Efficiency (kge_c2m)
• Bounded Modified Kling-Gupta Efficiency (kgeprime_c2m)
• Bounded Non-Parametric Kling-Gupta Efficiency (kgenp_c2m)

Finally, any of the objective functions can take an optional argument transform. This argument allows to apply a transformation on both the observed and the simulated streamflow time series prior the calculation of the objective function. The possible transformations are as follows:

• Inverted flows (using transform='inv')
• Square Root-transformed flows (using transform='sqrt')
• Natural Logarithm-transformed flows (using transform='log')

Dependencies

HydroEval requires the Python package numpy to be installed on the Python interpreter where hydroeval is installed.

Version History

• 0.0.3 [09 Sep 2019]: General enhancements
• 0.0.2 [29 Nov 2018]: General improvements
• 0.0.1 [26 Oct 2018]: First version of HydroEval

Acknowledgment

This tool was developed with the financial support of Ireland's Environmental Protection Agency (Grant Number 2014-W-LS-5).