To share or not to share: Drivers and barriers for sharing data via online amateur weather networks

Gharesifard, Mohammad; Wehn, Uta

doi:10.1016/j.jhydrol.2016.01.036

Cited by 54 publications

(57 citation statements)

References 30 publications

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“…A large number of PWSs share data on online platforms, both on the owner's own initiative (Gharesifard and Wehn, 2016) or automatically as an intrinsic software feature of the product (i.e., for Netatmo). Netatmo has its own online platform collecting and visualizing data from all operational Netatmo stations.…”

Section: Introductionmentioning

confidence: 99%

The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam

Vos

Leijnse

Overeem

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. The high density of built-up areas and resulting imperviousness of the land surface makes urban areas vulnerable to extreme rainfall, which can lead to considerable damage. In order to design and manage cities to be able to deal with the growing number of extreme rainfall events, rainfall data are required at higher temporal and spatial resolutions than those needed for rural catchments. However, the density of operational rainfall monitoring networks managed by local or national authorities is typically low in urban areas. A growing number of automatic personal weather stations (PWSs) link rainfall measurements to online platforms. Here, we examine the potential of such crowdsourced datasets for obtaining the desired resolution and quality of rainfall measurements for the capital of the Netherlands. Data from 63 stations in Amsterdam ( ∼ 575 km 2 ) that measure rainfall over at least 4 months in a 17-month period are evaluated. In addition, a detailed assessment is made of three Netatmo stations, the largest contributor to this dataset, in an experimental setup. The sensor performance in the experimental setup and the density of the PWS network are promising. However, features in the online platforms, like rounding and thresholds, cause changes from the original time series, resulting in considerable errors in the datasets obtained. These errors are especially large during low-intensity rainfall, although they can be reduced by accumulating rainfall over longer intervals. Accumulation improves the correlation coefficient with gauge-adjusted radar data from 0.48 at 5 min intervals to 0.60 at hourly intervals. Spatial rainfall correlation functions derived from PWS data show much more small-scale variability than those based on gauge-adjusted radar data and those found in similar research using dedicated rain gauge networks. This can largely be attributed to the noise in the PWS data resulting from both the measurement setup and the processes occurring in the data transfer to the online PWS platform. A double mass comparison with gauge-adjusted radar data shows that the median of the stations resembles the rainfall reference better than the real-time (unadjusted) radar product. Averaging nearby raw PWS measurements further improves the match with gauge-adjusted radar data in that area. These results confirm that the growing number of internet-connected PWSs could successfully be used for urban rainfall monitoring.

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Section: Introductionmentioning

confidence: 99%

The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam

Vos

Leijnse

Overeem

et al. 2017

Hydrol. Earth Syst. Sci.

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“…As discussed by Buytaert et al (2014) and Zheng et al (2018), it is challenging to attract a large number of participants for a crowdsourcing project, especially when the project is to last for a long period of time (Theobald et al, 2015). In response, beyond approaches relying solely on the voluntary participation of agents, researchers are now starting to examine the use of incentives to further enhance the motivation of the crowd (Gharesifard & Wehn, 2016;Newman et al, 2012;Tokarchuk et al, 2012). Studies on this matter include those about incorporating the design of task goals (e.g., the setting up of temporary goals or competitions), feedbacks, and rewards to attract participants (Gharesifard & Wehn, 2016;Le Coz et al, 2016;Reeves et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Reward‐Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent‐Based Model Simulation

Yang

Cai

2019

Water Resources Research

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Crowdsourcing incorporates common citizens as rich sources of data and is promising for environmental monitoring. In this paper, we propose and test the idea of incorporating incentives to crowdsourcing management for rainfall monitoring. Specifically, we model the allocation of incentives (quantitatively measurable and limited rewards) among crowdsourcing participants for a theoretical rainfall monitoring case. For this purpose, we develop an integrated model comprising a reward allocation component to represent the decision‐making process of a central manager, an agent‐based model to simulate the interactions between the manager and participants, and a rainfall simulation model to evaluate the effectiveness of various reward allocation policies. We simulate six reward allocation policies of varying levels of administrative cost, and consideration of participant and rainfall spatial heterogeneities. The results suggest the performance of each policy to improve with the reward budget and their spatial uniformity. Among the six policies tested, we find that the participant density weighted maximum participation policy yields the most accurate estimation of rainfall intensity due to its more explicit consideration of the spatial distribution of participants; however, this policy associates with a high administrative cost. This highlights the trade‐off between performance and cost in designing effective reward allocation policies. This paper provides a physical and behavior simulation modeling tool to study the feasibility and complexity of reward‐based participant management for crowdsourcing rainfall monitoring. The proposed crowdsourcing method is beneficial for a wide range of applications that require rainfall data with fine resolution, such as storm water management and water availability and biomass assessment for food and energy crops.

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“…Hobbs and White, 2012;Roy et al, 2012) and meteorology (e.g. Gharesifard and Wehn, 2016). It has been acknowledged that the idea of "the public" does not exist (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Citizens are motivated to continue to contribute by (increasing) the extent of their involvement (Rotman et al, 2012;Roy et al, 2012), offering feedback concerning the work at three levels (individual contribution, group contribution and the use of data) and building a relationship based on trust between scientists and citizens (Rotman et al, 2012). The importance of trust is stressed by authors in the field of water management as well (Buytaert et al, 2014;Gharesifard and Wehn, 2016).…”

Section: Introductionmentioning

confidence: 99%

Practitioners' viewpoints on citizen science in water management: a case study in Dutch regional water resource management

Minkman

Sanden

Rutten

2017

Hydrol. Earth Syst. Sci.

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Abstract. In recent years, governmental institutes have started to use citizen science as a form of public participation. The Dutch water authorities are among them. They face pressure on the water governance system and a water awareness gap among the general public, and consider citizen science a possible solution. The reasons for practitioners to engage in citizen science, and in particular those of government practitioners, have seldom been studied. This article aims to pinpoint the various viewpoints of practitioners at Dutch regional water authorities on citizen science. A Qmethodological approach was used because it allows for exploration of viewpoints and statistical analysis using a small sample size. Practitioners (33) at eight different water authorities ranked 46 statements from agree to disagree. Three viewpoints were identified with a total explained variance of 67 %. Viewpoint A considers citizen science a potential solution that can serve several purposes, thereby encouraging citizen participation in data collection and analysis. Viewpoint B considers citizen science a method for additional, illustrative data. Viewpoint C views citizen science primarily as a means of education. These viewpoints show water practitioners in the Netherlands are willing to embrace citizen science at water authorities, although there is no support for higher levels of citizen engagement.

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To share or not to share: Drivers and barriers for sharing data via online amateur weather networks

Cited by 54 publications

References 30 publications

The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam

The potential of urban rainfall monitoring with crowdsourced automatic weather stations in Amsterdam

Reward‐Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent‐Based Model Simulation

Practitioners' viewpoints on citizen science in water management: a case study in Dutch regional water resource management

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