As I mentioned last week, one of the most important aspects
of citizen science is its validity and whether the data can pass the stringent
scientific quality control. In this study, the researchers explored this
particular aspect and gave some valuable advice on the wider application of
community-based hydrological and meteorological monitoring programme.
This study is initiated by the AMGRAF (Adaptive Management
of shallow Groundwater for small-scale irrigation and poverty alleviation in
sub-Saharan AFrica) project where its main goal is to investigate the potential
of utilising shallow groundwater resources for small-scale irrigation and
poverty alleviation. This therefore links nicely with one of my goals of the
blog, which is to explore how the assessment of water resources come to inform
policy actions.
The location of the study is at Danila woreda, Ethiopia
(figure 1) and the installed monitoring points are shown in figure 2.
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| Figure 1. Study area. |
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| Figure 2. Locations of monitoring points (close to arrowhead in Figure 1). MW= monitoring well, DSC = Dangesheta Service Cooperative, DAO = Dangesheta Agricultural Office. |
Why was the community
involved?
There is very limited data available for rainfall, river
discharge and groundwater level. The researchers therefore seek help from the
local communities for data collection. Specific issues with formally recorded
data are outline below:
There is only one source precipitation data within the study
area, Dangila woreda. Although there are 8 other rain gauges outside the study
area, they all situate at different altitudes, limiting the ability to make
useful comparison. Gridded datasets could be an alternative source of data for
precipitation. However, as their resolution which ranges from 0.25◦ to 1.25◦ is
too coarse and comprise of a highly variable topography, it is likely that the
results may be compromised too.
The past river discharge data faced significant
inconsistency with gaps up to years. Additionally, two monitored river gauges
within Dangila woreda are no longer functioning and their existing records were
only inconsistently digitised. Furthermore, due to the lack of good quality
data, there is often no information on the level of peak flood and its timing.
As for groundwater level, there are only very limited data
on boreholes and groundwater available from formal sources. With community
monitored data, there is now information on water table depth and recession.
How the community was
involved?
The research team consulted the local community to identify
suitable place for installation of rain and river gauges and well monitors. Consequently,
the areas chosen were convenient for regular measurement by the community and
allowed good quality data to be collected (Figure 3). (e.g. avoiding areas where trees intercepting
rainfall from rain gauges and steeper channels where obvious river stage
fluctuations could be clearly captured by stage board)
There were also regular workshops throughout the project to
ensure the locals understand the results and the wider implication of the
research. The level of engagement is maintained through feedbacks and many even
actively noted down extra information (e.g. conditions and timings of peak flood)
which is extremely helpful for improving the hydrological understanding of this
data poor environment.
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| Figure 3. Photographs of monitoring in action. Left: Kilti river guage, middle: rain guage, and right: monitoring well. |
How are the data
judged?
The “Guide to climatological practices” (WMO,
2011) through the following tests checks the representativeness and accuracy of
measured data:
- Format tests:
error in dates and numbers
- Completeness
tests: whether the data (or lack thereof) is significant to the study
- Consistency tests
- internal
consistency checks: e.g. max rainfall > min rainfall
- temporal
consistency checks: e.g. regular time interval
- spatial
consistency checks: e.g. similar rainfall in neighbouring areas
- summarisation
consistency checks: e.g. monthly values adds up to correct annual values
- double mass
check: e.g. relationship between stage and discharge do not change if the
data is consistent
- comparison with
formally recorded values available
How successful was
it?
Figure 4 below clearly shows the community data performs
much better than the gridded datasets (TRMM, ERA-Interim, NASA-MERRA) across
all time periods in terms of correlation with daily rainfall from Dangila rain
gauge and has higher average of correlation, 0.73, than the expected value of
0.68. Also, the bias (best if closer to 1) is the lowest for the community data
(shown in Figure 5).
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| Figure 4. Pearson's Product Correlation Coefficient (PPC) between formally recorded daily rainfall and different sources. |
 |
| Figure 5. Bias between formally recorded daily rainfall and different sources. |
Although both river discharge and groundwater data lack
formally recorded data for validation, they are deemed ‘highly satisfactory’
after several statistical analysis. It is also realised that community data
could benefit from more frequent measurement to better capture the nature of
flash flood.
Summary and my reflection
Through this exercise, the shallow groundwater system of
this data poor area is much better understood. For example, areas with slow drops
of groundwater level after rainfall events signifies significant baseflow,
therefore a suitable site for potential groundwater extraction. The collected data
also proved to be much more applicable than the global gridded data set for
hydrological modelling on a local scale. Furthermore, the high correlation,
0.73, between collected and formally measured data also means that the
historical data could be used to extend the length of the model.
While the idea of stakeholder participation has gained
significant grounds in environmental management, to date there has been very
few researches on the role of citizen science in quantitative environmental
monitoring. This example showed that through early engagement with the local
communities from the inception of the project to data collection and analysis,
valuable data could be obtained easily by the everyone, especially in areas
where no alternative data exists! As it is usually areas like this that could
benefit most significantly through an inclusive and participatory hydrological research,
this has huge implications for the data poor area worldwide! Indeed, through the words of the Danila woreda
people, they feel longer the ‘subject of study’ but rather important partners
of the project. They take pride in their work from which other communities that
face similar issues can learn and benefit. This is an example of empowerment of
the local community, but also one that highlights the role scientists could
play in connecting the disparity between science and local policy (Ridder and
Pahl-Wostl, 2005).
