Citizen science in hydrology and water resources: opportunities for knowledge generation, ecosystem service management, and sustainable development

Abstract

The participation of the general public in the research design, data collection and interpretation process together with scientists is often referred to as citizen science. While citizen science itself has existed since the start of scientific practice, developments in sensing technology, data processing and visualization, and communication of ideas and results, are creating a wide range of new opportunities for public participation in scientific research. This paper reviews the state of citizen science in a hydrological context and explores the potential of citizen science to complement more traditional ways of scientific data collection and knowledge generation for hydrological sciences and water resources management. Although hydrological data collection often involves advanced technology, the advent of robust, cheap, and low-maintenance sensing equipment provides unprecedented opportunities for data collection in a citizen science context. These data have a significant potential to create new hydrological knowledge, especially in relation to the characterization of process heterogeneity, remote regions, and human impacts on the water cycle. However, the nature and quality of data collected in citizen science experiments is potentially very different from those of traditional monitoring networks. This poses challenges in terms of their processing, interpretation, and use, especially with regard to assimilation of traditional knowledge, the quantification of uncertainties, and their role in decision support. It also requires care in designing citizen science projects such that the generated data complement optimally other available knowledge. Lastly, using 4 case studies from remote mountain regions we reflect on the challenges and opportunities in the integration of hydrologically-oriented citizen science in water resources management, the role of scientific knowledge in the decision-making process, and the potential contestation to established community institutions posed by co-generation of new knowledge.

Improving Flood Prediction Assimilating Uncertain Crowdsourced Data into Hydrological and Hydraulic Models

“…A detailed and interesting review of the examples of citizen science applications in hydrology and water resources science is reported by Buytaert et al (2014). In this study it is pointed out that, in most cases, the final scope of citizen activities is connected to water quality.…”

“…The main advance of using these type of sensors is that they can be used not only by technicians, as for observations from traditional physical sensors, but also by regular citizens, and that due to their reduced cost, a more spatially distributed coverage can be achieved. Recently, citizen science activities have been widely promoted in order to collect crowdsourced (CS) observations of hydrological variables ,generate additional knowledge of the water cycle, and use such knowledge in decision making (Bonney et al, 2014;Buytaert et al, 2014). Howe (2008) defined the concept of crowdsourcing as "the act of taking a job traditionally performed by a designated agent (usually an employee) and outsource it to an undefined, generally large group of people in the form of an open call".…”

“…At the same time, opportunities to use citizen science to achieve positive outcomes for science and society are going unrealized." Buytaert et al (2014) pointed out that motivations of citizen engagement vary according to geographical location. In fact, citizen science projects in wealthy regions aim to increase awareness and scientific literacy, while in developing regions the main goals are more related to enhancement of community well-being such poverty alleviation (Gura, 2013).…”

“…As described in the previous section, DA applications require specific, frequent and high quality measurements, which may not be compatible with the distributed, intermittent and, potentially, lower-quality nature of citizen-based data (Shanley et al, 2013;Buytaert et al, 2014;Lahoz and Schneider, 2014). That is why interpolation and merging techniques are commonly used to integrate citizen observations within mathematical models.…”

“…In fact, only a few recent studies considered the integration of crowdsourced observations with water-related models (Buytaert et al, 2014). Some applications, as mentioned before, are related to data fusion appliedto air quality sensors .…”