6.4.2 Possible Practical Applications
Considering the similar findings across all groups and resilience dimensions/elements (Sect. 6.4.1), there is a dire need to increase the adaptive capacity of the
vulnerable local communities. We argue that both the data generated from this study
and the underlying research approach can be used to inform the development of
interventions for increasing community resilience to floods and droughts.
First, our observations during fieldwork and the study findings suggest that local
communities have indeed a rather good understanding of their (lack of) resilience for
specific sub-dimensions and elements. Such insights can be used to identify priority
thematic areas for interventions. At the same time, this makes the case that collective
and participatory processes can inform the development of local adaptation options
for floods and droughts. Second, the knowledge about the disparities among groups
and localities can provide finer-grain information to develop more tailored interventions for particularly vulnerable regions and groups.
Our overall methodology was transdisciplinary, participatory and contextspecific, relying heavily on baseline information from vulnerable socio-ecological
systems. This bottom-up approach, when employed properly, allows for incorporating diverse community viewpoints and lived experiences in the resilience assessment
process. Apart from providing useful information for the development of appropriate
interventions, such information can also assist in during the decision-making. By
involving communities in this manner, it gives offers them greater power and a voice
in decisions that truly affect them. This bodes well with recommendations that
interventions seeking to increase the resilience of local communities to climatic
hazards should allow for the participation of vulnerable groups during the design,
implementation and evaluation of such interventions (UNFCCC 2018; Norris et al.
2008). One added benefit of this methodology is that it is rapid, requires little
expertise and can be implemented with few resources. In this sense, it can act as a
quick pre-planning tool to identify broad community patterns and priority intervention areas. Furthermore, it is rather flexible and can offer complementary information
to seasonal prediction models and other more technical tools aiming to build
resilience to climatic hazards.
However, despite their strengths, our results and underlying methodology have
limitations. First, there seems to be a trade-off between speed/cost-effectiveness and
accuracy. This trade-off is quite common for analytical techniques that depend on
self-reported and qualitative data like the one reported in this study. Apart from the
general methodological issues, there should be some caution when generalizing the
actual results of this study. We used non-probabilistic sampling, which possibly
resulted in the unequal representation of some respondents in the FGDs. Second, in
our attempt to reduce the number of FGDs per site (and thus the needed time/
resources), we did not include a distinct FGD group consisting of respondents
above 70 years old, a group that is considered as extremely vulnerable to climatic
hazards in the study area (Zimmerman et al. 2007; Haq et al. 2008). Hence, there is
large variation in respondent ages within the elderly groups, which has possibly
212
Y. A. Boafo et al.
Considering the similar findings across all groups and resilience dimensions/elements (Sect. 6.4.1), there is a dire need to increase the adaptive capacity of the
vulnerable local communities. We argue that both the data generated from this study
and the underlying research approach can be used to inform the development of
interventions for increasing community resilience to floods and droughts.
First, our observations during fieldwork and the study findings suggest that local
communities have indeed a rather good understanding of their (lack of) resilience for
specific sub-dimensions and elements. Such insights can be used to identify priority
thematic areas for interventions. At the same time, this makes the case that collective
and participatory processes can inform the development of local adaptation options
for floods and droughts. Second, the knowledge about the disparities among groups
and localities can provide finer-grain information to develop more tailored interventions for particularly vulnerable regions and groups.
Our overall methodology was transdisciplinary, participatory and contextspecific, relying heavily on baseline information from vulnerable socio-ecological
systems. This bottom-up approach, when employed properly, allows for incorporating diverse community viewpoints and lived experiences in the resilience assessment
process. Apart from providing useful information for the development of appropriate
interventions, such information can also assist in during the decision-making. By
involving communities in this manner, it gives offers them greater power and a voice
in decisions that truly affect them. This bodes well with recommendations that
interventions seeking to increase the resilience of local communities to climatic
hazards should allow for the participation of vulnerable groups during the design,
implementation and evaluation of such interventions (UNFCCC 2018; Norris et al.
2008). One added benefit of this methodology is that it is rapid, requires little
expertise and can be implemented with few resources. In this sense, it can act as a
quick pre-planning tool to identify broad community patterns and priority intervention areas. Furthermore, it is rather flexible and can offer complementary information
to seasonal prediction models and other more technical tools aiming to build
resilience to climatic hazards.
However, despite their strengths, our results and underlying methodology have
limitations. First, there seems to be a trade-off between speed/cost-effectiveness and
accuracy. This trade-off is quite common for analytical techniques that depend on
self-reported and qualitative data like the one reported in this study. Apart from the
general methodological issues, there should be some caution when generalizing the
actual results of this study. We used non-probabilistic sampling, which possibly
resulted in the unequal representation of some respondents in the FGDs. Second, in
our attempt to reduce the number of FGDs per site (and thus the needed time/
resources), we did not include a distinct FGD group consisting of respondents
above 70 years old, a group that is considered as extremely vulnerable to climatic
hazards in the study area (Zimmerman et al. 2007; Haq et al. 2008). Hence, there is
large variation in respondent ages within the elderly groups, which has possibly
212
Y. A. Boafo et al.