Monitoring to Learn, Learning to Monitor: A Critical Analysis of Opportunities for <scp>I</scp>ndigenous Community‐Based Monitoring of Environmental Change in Australian Rangelands

Wiseman, Nathanael D.; Bardsley, Douglas K.

doi:10.1111/1745-5871.12150

Cited by 26 publications

(14 citation statements)

References 67 publications

(122 reference statements)

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“…The higher number of articles describing collaborative monitoring could be explained by the broader trend of engaging Indigenous communities in research and monitoring practices, whereas externally-driven monitoring is likely influenced by conventional, government or researcher implemented monitoring approaches [110], as well as requests by Indigenous communities for external monitoring support [53,94]. However, successful CBM generally occurs when communities monitor things they personally connect with and care about, rather than for externally-driven needs [111,112]. Moreover, in the context of climate research, studies initiated with and by Indigenous community members tend to indicate more responsible community engagement than studies initiative by outside researchers alone [113].…”

Section: Externally Driven Monitoringmentioning

confidence: 99%

Community-based monitoring of Indigenous food security in a changing climate: global trends and future directions

Lam

Dodd

Skinner

et al. 2019

Environ. Res. Lett.

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Climate change is expected to exacerbate existing food security challenges, especially in Indigenous communities worldwide. Community-based monitoring (CBM) is considered a promising strategy to improve monitoring of, and local adaptation to climatic and environmental change. Yet, it is unclear how this approach can be applied in food security or Indigenous contexts. The objectives of this paper are to: (1) review and synthesize the published literature on CBM of Indigenous food security; and, (2) identify gaps and trends in these monitoring efforts in the context of climate change. Using a systematic search and screening process, we identified 86 published articles. To be included, articles had to be published in a journal, describe a CBM system, describe any aspect of food security, and explicitly mention an Indigenous community. Relevant articles were thematically analyzed to characterize elements of CBM in the context of climate change. Results show that the number of articles published over time was steady and increased more than two-fold within the last five years. The reviewed articles reported on monitoring mainly in North America (37%) and South America (28%). In general, monitoring was either collaborative (51%) or externally-driven (37%), and focused primarily on tracking wildlife (29%), followed by natural resources (16%), environmental change (15%), fisheries (13%), climate change (9%), or some combination of these topics (18%). This review provides an evidence-base on the uses, characteristics, and opportunities of CBM, to guide future food security monitoring efforts in the context of climate change.

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Section: Externally Driven Monitoringmentioning

confidence: 99%

Community-based monitoring of Indigenous food security in a changing climate: global trends and future directions

Lam

Dodd

Skinner

et al. 2019

Environ. Res. Lett.

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show abstract

“…It is, therefore, useful to also consider the use of other approaches to obtain water level data. Citizen science is now more frequently used to obtain environmental data over large areas (Savan et al, 2003;Bonney et al, 2009;Graham et al, 2011;Fohringer et al, 2015;Huddart et al, 2016;Wiseman and Bardsley, 2016). Little et al (2016) gave citizen scientists water level sounders to measure groundwater levels in private wells and found that these measurements provided valuable data on groundwater levels across a large area in Alberta, Canada, and that the measurements were relatively accurate; the root mean square error between citizen scientist observed water levels and pressure transducer based water levels ranged between 3 and 11 cm.…”

Section: Introductionmentioning

confidence: 99%

Information content of stream level class data for hydrological model calibration

Meerveld

Vis

Seibert

2017

Hydrol. Earth Syst. Sci.

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Abstract. Citizen science can provide spatially distributed data over large areas, including hydrological data. Stream levels are easier to measure than streamflow and are likely also observed more easily by citizen scientists than streamflow. However, the challenge with crowd based stream level data is that observations are taken at irregular time intervals and with a limited vertical resolution. The latter is especially the case at sites where no staff gauge is available and relative stream levels are observed based on (in)visible features in the stream, such as rocks. In order to assess the potential value of crowd based stream level observations for model calibration, we pretended that stream level observations were available at a limited vertical resolution by transferring streamflow data to stream level classes. A bucket-type hydrological model was calibrated with these hypothetical stream level class data and subsequently evaluated on the observed streamflow records. Our results indicate that stream level data can result in good streamflow simulations, even with a reduced vertical resolution of the observations. Time series of only two stream level classes, e.g. above or below a rock in the stream, were already informative, especially when the class boundary was chosen towards the highest stream levels. There was some added value in using up to five stream level classes, but there was hardly any improvement in model performance when using more level classes. These results are encouraging for citizen science projects and provide a basis for designing observation systems that collect data that are as informative as possible for deriving model based streamflow time series for previously ungauged basins.

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“…It is, therefore, useful to also consider the use of other approaches to obtain water level data. Citizen science is now more frequently used to obtain environmental data over large areas (Bonney et al, 2009;Fohringer et al, 2015;Graham et al, 2011;Huddart et al, 2016;Savan et al, 2003;Wiseman and Bardsley, 2016). Little et al (2016) gave citizen scientists water level sounders to measure groundwater levels in private wells and found that these measurements provided valuable data on groundwater levels across a large area in Alberta, Canada, and that the measurements were relatively accurate; the root mean square error between citizen scientist observed water levels and pressure transducer based water levels ranged between 3 and 11 cm.…”

mentioning

confidence: 99%

Information content of stream level class data for hydrological model calibration

Meerveld¹,

Vis²,

Seibert³

2017

Preprint

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Abstract. Citizen science can provide spatially distributed data over large areas, including hydrological data. Stream levels are easier to measure than streamflow and can be observed more easily by citizen scientists. However, the challenge with crowd-based stream level data is that observations are taken at irregular time intervals and with a limited vertical resolution. The latter is especially the case at sites where no staff gauge is available and relative stream levels are observed based on (in)visible features in the stream, such as rocks. In order to assess the potential value of crowd-based stream level observations for model calibration, we pretended that stream level observations were available at a limited vertical resolution by transferring streamflow data into stream level classes. A bucket-type hydrological model was calibrated with these hypothetical data sets and subsequently evaluated on the observed streamflow records. Our results indicate that stream level data can result in good streamflow simulations, even with a reduced vertical resolution of the observations. Time series of only two stream level classes, e.g. above or below a rock in the stream, were already informative, especially when the class boundary was chosen towards the highest stream levels. There was some added value in using up to five stream level classes but there was hardly any improvement in model performance when using more level classes. These results are encouraging for citizen science projects and provide a basis for designing observation systems that collect data that are as informative as possible for deriving model-based streamflow time series for previously ungauged basins.

show abstract

Monitoring to Learn, Learning to Monitor: A Critical Analysis of Opportunities for Indigenous Community‐Based Monitoring of Environmental Change in Australian Rangelands

Cited by 26 publications

References 67 publications

Community-based monitoring of Indigenous food security in a changing climate: global trends and future directions

Community-based monitoring of Indigenous food security in a changing climate: global trends and future directions

Information content of stream level class data for hydrological model calibration

Information content of stream level class data for hydrological model calibration

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