Paper short abstract:

Enabling citizen science by collecting real-time hydrometric data using robust, low-cost sensor networks generates locally actionable knowledge, and empowers local stakeholders, to build resilience to natural hazards. The use of citizen science in polycentric risk management practices is discussed.

Paper long abstract:

In managing the risks associated with hydrological hazards (e.g. floods, drought), non-specialist local stakeholders have arguably always played an important role by providing empirical risk knowledge and supporting resilience building. With the rapid growth of highly connected societies utilising technologies such as the Internet, smartphones, and social media, 'crowdsourcing' data has been used to monitor evolving risks (e.g. http://www.globalfloodsystem.com/). However, such approaches do not fully engage with the capability and capacity of local communities to perform citizen science (or "collaborative learning"), thereby making knowledge creation more multidirectional, decentralized, diverse, and inclusive.

This paper discusses an emerging direction in citizen science by enabling local non-scientist stakeholders to collect real-time hazard data using robust and low-cost sensor networks. The establishment of such networks is outlined, highlighting the need for a clear definition of their purpose(s), along with a profound understanding of the motivations and skillsets of all participants and stakeholders.

We show that such networks have great potential to enhance local knowledge co-creation and can serve as a means of educating and empowering communities and stakeholders that are bypassed by more traditional knowledge generation processes. We then discuss such citizen science initiatives within the context of polycentric risk management; in particular, how they can complement and augment more traditional knowledge generation practices (for example, by enabling increased preparedness through local forecasting), as well as top-down risk management approaches often practised in e.g. flood early-warning systems.