Next Article in Journal
The Effect of Climate Change on Loss of Lake Volume: Case of Sedimentation in Central Rift Valley Basin, Ethiopia
Next Article in Special Issue
Eye in the Sky: Using UAV Imagery of Seasonal Riverine Canopy Growth to Model Water Temperature
Previous Article in Journal
Submarine Groundwater Discharge Differentially Modifies Photosynthesis, Growth, and Morphology for Two Contrasting Species of Gracilaria (Rhodophyta)
Previous Article in Special Issue
An Empirical Mode-Spatial Model for Environmental Data Imputation
Article Menu

Export Article

Open AccessArticle
Hydrology 2018, 5(4), 66; https://doi.org/10.3390/hydrology5040066

Hydrostats: A Python Package for Characterizing Errors between Observed and Predicted Time Series

Department of Civil and Environmental Engineering, Brigham Young University, Provo, UT 84602, USA
*
Author to whom correspondence should be addressed.
Received: 30 September 2018 / Revised: 26 November 2018 / Accepted: 29 November 2018 / Published: 2 December 2018
  |  
PDF [1815 KB, uploaded 7 December 2018]
  |  

Abstract

Hydrologists use a number of tools to compare model results to observed flows. These include tools to pre-process the data, data frames to store and access data, visualization and plotting routines, error metrics for single realizations, and ensemble metrics for stochastic realizations to calibrate and evaluate hydrologic models. We present an open-source Python package to help characterize predicted and observed hydrologic time series data called hydrostats which has three main capabilities: Data storage and retrieval based on the Python Data Analysis Library (pandas), visualization and plotting routines using Matplotlib, and a metrics library that currently contains routines to compute over 70 different error metrics and routines for ensemble forecast skill scores. Hydrostats data storage and retrieval functions allow hydrologists to easily compare all, or portions of, a time series. For example, it makes it easy to compare observed and modeled data only during April over a 30-year period. The package includes literature references, explanations, examples, and source code. In this note, we introduce the hydrostats package, provide short examples of the various capabilities, and provide some background on programming issues and practices. The hydrostats package provides a range of tools to make characterizing and analyzing model data easy and efficient. The electronic supplement provides working hydrostats examples. View Full-Text
Keywords: hydrology; water resource engineering; Python; error metrics; statistics; predicted and observed flows hydrology; water resource engineering; Python; error metrics; statistics; predicted and observed flows
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary materials

SciFeed

Share & Cite This Article

MDPI and ACS Style

Roberts, W.; Williams, G.P.; Jackson, E.; Nelson, E.J.; Ames, D.P. Hydrostats: A Python Package for Characterizing Errors between Observed and Predicted Time Series. Hydrology 2018, 5, 66.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Hydrology EISSN 2306-5338 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top