Preparing GIS data for analysis of stream monitoring data: The R package openSTARS.

Mira Kattwinkel,Eduard Szöcs,Erin Peterson,Ralf B Schäfer,Stoyan Nedkov

doi:10.1371/journal.pone.0239237

Abstract

Stream monitoring data provides insights into the biological, chemical and physical status of running waters. Additionally, it can be used to identify drivers of chemical or ecological water quality, to inform related management actions, and to forecast future conditions under land use and global change scenarios. Measurements from sites along the same stream may not be statistically independent, and the R package SSN provides a way to describe spatial autocorrelation when modelling relationships between measured variables and potential drivers. However, SSN requires the user to provide the stream network and sampling locations in a certain format. Likewise, other applications require catchment delineation and intersection of different spatial data. We developed the R package openSTARS that provides the functionality to derive stream networks from a digital elevation model, delineate stream catchments and intersect them with land use or other GIS data as potential predictors. Additionally, locations for model predictions can be generated automatically along the stream network. We present an example workflow of all data preparation steps. In a case study using data from water monitoring sites in Southern Germany, the resulting stream network and derived site characteristics matched those constructed using STARS, an ArcGIS custom toolbox. An advantage of openSTARS is that it relies on free and open-source GRASS GIS and R functions, unlike the original STARS toolbox which depends on proprietary ArcGIS. openSTARS also comes without a graphical user interface, to enhance reproducibility and reusability of the workflow, thereby harmonizing and simplifying the data pre-processing prior to statistical modelling. Overall, openSTARS facilitates the use of spatial regression and other applications on stream networks and contributes to reproducible science with applications in hydrology, environmental sciences and ecology.

Highlights

Streams and rivers are regularly monitored to assess their biological, chemical and physical status
The derived attributes catchment size and area of arable land use within the catchments of the sites derived with the two tools are very similar, when based on the original stream network
There are only two exceptions: one site was snapped to a smaller tributary created in openSTARS, which is lacking in the streams dataset used in STARS, and in the other case the network is smaller (S1 File)

Summary

Introduction

Streams and rivers are regularly monitored to assess their biological (e.g. species composition or abundance), chemical (e.g. nutrient or pesticide concentrations) and physical (e.g. temperature) status. BASF SE provided support in the form of salaries for author ES at time of preparation of the manuscript, but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Improve water quality led to vast monitoring efforts to assess the status of European water bodies, comprising a monitoring network of more than 67000 sites in 2012 [1] This extensive network is complemented by additional national, regional or local stream monitoring programs; for example to evaluate pesticide concentrations or to monitor industrial discharge [2]. To the best of our knowledge, all of the applications used the Spatial Tools for the Analysis of River Systems (STARS) toolbox [14] for preparing the spatial input data to allow for subsequent modelling in SSN. We provide example code in the S2 File, enabling readers to recreate this workflow using their own stream data, and compare openSTARS with STARS output

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