How can we integrate indigenous knowledge into anticipatory actions for disaster risk reduction amid climate change?

Let's critically examine the paper titled "Incorporating Indigenous Knowledge Systems-Based Climate Services in Anticipatory Action in Zimbabwe: An Ex-ante Assessment" by Dube et al. (2024).

Anticipatory action (AA) is a crucial and pivotal model within the realm of disaster risk financing, firmly based on the principles of disaster preparedness. AA utilises forecasting techniques to activate predetermined responses and efficiently allocate resources. In this framework, indigenous knowledge, obtained from communities deeply affected by climate change and their corresponding responses, should assume a fundamental and indispensable role.

I love how the authors describe "indigenous knowledge as the sum of facts and place-based knowledge known or learned from cumulative day-to-day experience or acquired through cumulative repetitive observation and experience." This definition emphasises the vibrancy of indigenous knowledge as a "sum of facts and place-based knowledge" in both spatial and temporal dimensions. In this paper, the authors argue that communities in Zimbabwe are predisposed to using both indigenous and contemporary climate information in the studied regions, which account for more than 82% of the population.

Simultaneously, the authors discovered that, while data collection for contemporary climate information has improved, this sophistication and pluralistic nature of data does not address the issues of interoperability, accessibility, and the use of current forecasts to initiate anticipatory actions to mitigate climate risks such as droughts and floods at the local level. Local actors, like pastoralists and farmers, use specialised knowledge about plant phenology and animal behaviour to forecast and make decisions at the local level. This instance is not unique to Zimbabwe. The use of indigenous knowledge systems is common throughout Africa.

The authors of this paper contend that the trustworthiness of the indicators employed by local actors, the accessibility of this knowledge, and the resolution of current data—which does not offer locally meaningful data—are the primary causes of this prevalence at the local level. The author also contends that the indigenous knowledge system suffers from spurious weaknesses such as a lack of accuracy, in which income rain is described as "rain" without providing accurate information to support this argument, such as minimal rainfall, medium rainfall, or extreme rainfall, making it difficult to use as an anticipatory trigger due to the issue of uncertainty.

The system also suffers from a lack of lead time (a critical time when forecasts are made and when the initial disaster occurs), which is a critical point in determining when to initiate actions such as food aids and cash transfers. On the flip side, the authors argue that indigenous knowledge is more relevant locally than current data. Its accessibility allows local actors to easily interpret it. The author encourages the inclusion of indigenous knowledge systems to supplement the already organised and documented modern climate information services.

The authors argue that it is critical to begin documenting the triggers and indicators by conducting a longitudinal study. The author concludes by stating that including indigenous knowledge and contemporary climate data should be triangulated because it provides two significant benefits to the anticipatory space. First, triangulation helps with two things. First, it helps us to notice if communities are observing the same things that we are observing through the scientific method. Second, it helps with buy-in when you use their methods.

Does this sound like an effective way to incorporate indigenous systems into modern climate information systems?