Exploring the Use of Decision Tree Methodology in Hydrology Using Crowdsourced Data

Abstract

To fill the observations gap on ungauged streams, crowdsourced distributed hydrologic measurements were considered as a potential supplement for observational data networks. However, citizen science data come with uncertainty as they are provided by the general public. In order to investigate this uncertainty, a decision tree methodology was applied to evaluate existing citizen science data of stream stage based on the CrowdHydrology (CH) network. Quality control (QC) flags were developed and applied to CH sites, dividing Level 1 dataset (raw dataset) into Level 2 (flagged dataset) and Level 3 (processed dataset). Error estimates were calculated to determine uncertainty in the citizen science data. The results indicate that the decision tree could provide reliable QC for citizen science data and demonstrate how uncertainty can be quantified in the QC datasets.

Featured Collection Introduction: National Water Model III

“…The final paper in this collection by Wu et al (2021) is somewhat unique from papers in previous featured collections but could supplement the work of Brackins et al (2021) as relates to channel geometry data collected by volunteers. Recognizing that (1) data collected by agencies are limited (2) these data could be supplemented by data collected by volunteers (Kampf et al 2018), and (3) data collected by volunteers could have, or be perceived to have, less reliability than agency‐collected data, Wu et al explored approaches for determining the uncertainty in these types of data.…”

Stream Stage Monitoring with Community Science-Contributed Stage Data

“…One concern with use of contributed data is data quality [13] and assessing uncertainty in data [12]. Using data from the CrowdHydrology network [6], a decision tree approach was applied to evaluate and clean submitted data, yielding estimates of data uncertainty [14]. In a separate study, data quality in community science were examined and it was concluded that datasets produced by volunteers can be of comparable quality to those produced by professionals, provided methods to boost accuracy and account for bias are in place [2].…”