Growing Pains of Crowdsourced Stream Stage Monitoring Using Mobile Phones: The Development of CrowdHydrology

Abstract

Citizen science-based approaches to monitor the natural environment tend to be bimodal in maturity. Older and established programs such as the Audubon's Christmas bird count and Community Collaborative Rain, Hail, and Snow Network (CoCoRaHS) have thousands of participants across decades of observations, while less mature citizen science projects have shorter lifespans often focused on local or regional observations with tens or hundreds of participants. For the latter, it can be difficult to transition into a more mature and sustainable citizen science-based research program. This paper focuses on this transition by evaluating CrowdHydrology (ca. 2010), a citizen science project that has transitioned from a regional to national network. It evaluates the data accuracy, citizen participation, and station popularity. The CrowdHydrology network asks citizens to send in text messages of water levels in streams and lakes, which has resulted in 16,294 observations submitted by over 8,000 unique participants at 120 unique locations. Using water level data and participation records from CrowdHydrology, we analyze the expansion and citizen participation from a regional to national citizen science network. We identify barriers to participation and evaluate why some citizen science observation stations are popular while others are not. We explore our chosen contributory program model for CrowdHydrology and the influence this model has had on long-term participation. Results demonstrate a highly variable rate of contributions of citizen scientists. This paper proposes hypotheses on why many of our observations are from one-time participants and why some monitoring stations are more popular than others. Finally, we address the future expansion of the CrowdHydrology network by evaluating successful monitoring locations and growing interest of watershed groups to expand the network of gauges.

Quality and timing of crowd‐based water level class observations

“…For novice contributors mistakes are also more likely. The virtual staff gauge approach is harder to understand than the approach of CrowdHydrology (Lowry et al, 2019) or the project in Kenya by Weeser et al (2018), where water levels are read from physical staff gauges in, for instance, centimetres. This may contribute to poorer data quality for novice contributors, and may explain the lower correlation with the measured water levels for the pen‐and‐paper contributions.…”

“…Examples of citizen observatories are WeSenseIt (http://www.wesenseit.com; Lanfranchi, Wrigley, Ireson, Wehn, & Ciravegna, 2014), GroundTruth2.0 (https://gt20.eu) and SCENT (https://scent-project.eu). Some of the existing examples of citizen science projects that collect streamflow or water level data for ungauged streams are CrowdHydrology (Lowry, Fienen, Hall, Stepenuck, & Paul, 2019), a project in Kenya (Weeser et al, 2018), CitHyd (http://www.cithyd.com, Balbo & Galimberti, 2016) in Italy, SmartPhones4Water in Nepal (http://www.smartphones4water.org; Davids, van de Giesen, & Rutten, 2017) and CrowdWater (http://www.crowdwater.ch; Seibert, Strobl, Etter, Hummer, & van Meerveld, 2019).…”

Can the Quality of the Potential Flood Risk Maps be Evaluated? A Case Study of the Social Risks of Floods in Central Spain

“…Although the above could be considered as a bias in the spatio-temporal distribution of data associated with 112 phone calls, the magnitude and the importance of this bias were not comparable to those currently associated with the use of citizen or crowd-source data [30,56], which is higher by one or two orders of magnitude. On the other hand, the use of citizen sources is more common in studies focused on natural hazards assessment (disasters detection and real time monitoring), not for integrated risk assessment (including damages and losses).…”

“…However, it has been observed that the use of data from social networks has its disadvantages [28][29][30]. From the results of other recent studies [56], a bias in crowd-source data availability and spatial distribution could be denoted, even in projects that enjoyed an important degree of maturity, as in the case of the CrowdHydrology project in the USA. In this sense, the use of information from Emergency Services solves some of these weak points, such as the different degree of implementation and the use of social networks based on age, gender, and location (rural or urban environment).…”

Development of a Metric of Aquatic Invertebrates for Volunteers (MAIV): A Simple and Friendly Biotic Metric to Assess Ecological Quality of Streams

“…In general, CS has been used in hydrological observations and/or in the monitoring of surface water quality in all different continents of the globe. This is the case of Europe [13], North America [14], Central America [15], South America [11], Oceania [16], Asia [17], and Africa [18], demonstrating that CS is a low-cost and crucial tool in raising awareness on the importance of good water quality [13], and important in decision-making instances [19]. When educators are involved in CS, the benefits for school environmental education are multiple [20,21].…”