Step 3: Identifying Hazards

Submitted by Sukaina Bharwani | published 25th Mar 2011 | last updated 30th Mar 2011

Spatial Dynamics

Developing a seasonal calendar in Mali

Developing a seasonal calendar in Mali

Further details of the climatic hazards should be investigated to include:

  • A narrative that describes the effects of the hazard. This can help support projects by indicating what are the main concerns with the hazard.
  • The duration and spatial extent of the hazard. Note if specific regions are at higher risk.
  • The expected frequency of occurrence. The conventional notation is an annual expectation (e.g., a 5% chance of occurring in any one year) which can be converted into the equivalent return period (e.g., a 5% probability is often expressed as the event which occurs once in 20 years)

At this stage, precise details of the hazards may not be necessary. For example, there are hundreds of definitions of drought. Further research on the climatic hazards may be warranted later, but the focus of this step is on present risks rather than long term prospects.

The aim of this stage if using GIS technology is to translate indicators of vulnerability for specific exposure units/stresses (e.g populations at risk of drought) into vulnerability maps, and then define hotpots and indicators of aggregate vulnerability using foundation datasets such as the one available for Mali, which highlight the zones within the country that face exposure to specific hazards.

Temporal Dynamics

Climatic variability poses significant repercussions for agricultural production, but its spatial and temporal manifestations are considerably varied. The issues before agricultural policy in the face of climate change are complex enough that misunderstanding the full ramifications of events such as temperature extremes, or for that matter, a trend through a specific period such as the 1990s, will have significant impact at the farm level. Disease, pests, droughts and large storms, these are issues of great importance to agriculture and they appear to vary both in space and in time. Understanding local patterns in the context of the immediate region will help guide the development of viable coping mechanisms, from agronomic practices to crop insurance, in the face of uncertainty regarding both the direction of climate change trends and its magnitude.

The diagnostic capacity to investigate these impacts can be significantly increased by coupling detailed historical meteorological data with innovative analytic methods. On the basis of available data and information, it is possible to analyze the conditions and trends in climate parameters, from the most basic data (e.g. maximum and minimum temperature and rainfall), to more elaborate indicators (duration of the growing season), to complex indices (satisfaction index of water requirements for the growing season) to allow the identification of important thresholds and trigger points on short and medium time scales. This information can be used to assess potentially impacts and identify anticipatory adaptation measures. An analysis of changes in the length of growing season over southern Africa provides an example of this approach.

A useful starting point is to develop a seasonal calendar for the region (as illustrated below). The seasonal calendar presented here provides guidance for the identification of climate relevant time periods (key dates in terms of climatic thresholds) for cropping cycles. Further exploration, for example, of a changing onset of the growing season would focus attention on the months of April through May, for this particular case.

Seasonal Calendar for Maize Growing Regions in Mali.  Adapted from FEWS data

Process

- Identify project sites and similar regions. Identify locations and spatial extent of major climate hazards. Locate vulnerable groups see Step 2- NAPA)
- Assess current concerns in historical climate context
- Identify hotspots where higher-resolution work should be conducted
- Use historical climate data with climate thresholds and sensitive periods (e.g. in growing season or hydrological system) to assess current risk and identify sensitive points in the system. Models may be required to assess complex impacts.