The aim of this work was to study droughts that occur in the Bani basin (Mali) using indexes at different time steps. Indexes were computed because they are useful in comparing different situations over time and space and also because they give a standardized definition of droughts.
The computed CMI using monthly PET and rainfall data provides the first indication of the water stress with an increasing trend all over the basin after 1970, the critical year in this region. Indeed, the part of the basin classified as humid for the years 1940–1970 disappeared for the years 1971–1995 and the arid zone got much larger.
Further drought analysis was provided using the SPI at a 10-day time step. Although it was totally unsuccessful when computed with the ‘classical’ definition, it provided good information with a ‘degraded’ definition: MDD and precipitation threshold for each drought category. Unfortunately, important information such as mean drought duration was very imprecise.
The inadequacies of the SPI call for the usage of another index, namely the EDI, which is more complicated but more robust with daily data analysis and computation. Moreover, the EDI does not use a statistical distribution like the gamma distribution for the SPI. Thus, the EDI does not reject any station that increases its accuracy. This index also gives very interesting data for agriculture purposes like mean and MDD. In order to have a quick overview of regions threatened by increasing drought indices, we produced a drought assessment rank map of the basin,
Three main threatened regions identified are as follows: the whole southern part of the basin (i.e. south of the 1200 mm annual isohyet), the north-eastern part of the Bani Basin (Dionkele, Zangasso, Klela, etc.) and the central western part (Fana, Dioila, Beleko, etc.).
Owing to this drought assessment rank, it is now possible to focus first on most threatened regions for creating coping strategies such as irrigation, weather insurances, or seasonal forecasts.
Moreover, it is evident that the drought situation is getting worse after 1970 and that there is an increase in the number of days being classified as drought days. Indeed, if the number of events is decreasing, their duration is increasing. This tendency is especially obvious considering all types of droughts (EDI < 0). This supports the findings made with the CMI.
Finally, the EDI provides a better tool for studying droughts than SPI-10d. The EDI also allows a more accurate description of the spatial extension of the drought than other indices like, for instance, the variable ‘days without any rainfall’, which seems too simple and with its use we would have to skip the extension of the drought in the south especially. Indeed, this simple approach is not ‘site located’, which means that a drought is defined relatively to the rest of the basin. From this point of view, a 5-day drought has the same impact in the south and in the north. This is obviously wrong, as crops are very different, especially considering their drought tolerance (e.g. maize vs millet).
Using such an index seems to be very useful for agricultural purposes. For example, insurance systems based on weather information are now being used more frequently in African countries (e.g. in Malawi) and, according to Berg et al. (2008), there is a real need to use a robust daily or decadal index.
It is important to be aware of the study limitations, which are mainly due to lack of data as there is no precise way to fill lacks of data on a daily time step. Hence, the results, especially values, must be taken with caution.
Another limitation is the rainy period chosen, which spreads over the months of July, August, and September. Although it is obvious that these 3 months constitute the rainy period, the rainy season can last longer than 3 months in some regions, especially in the south. Hence, the result is more for the months of July, August, and September rather than for ‘the rainy season’.
It is interesting to study the relative usefulness of the new indicator (EDI) more precisely, as it has been done with the SPI. As noted, one of the main limitations of the EDI was the time required to compute it and also its definition, with a lot of parameters. It is therefore necessary to write a script with free access for better computation of the index in a more efficient way (a package capable of computing several drought indicators on a monthly time step already exists: SPATSIM, but it is not free (Smakhtin and Hughes, 2005). These kind of indicators have to be simple and user friendly.
Regarding the rainy period, it could be useful to use a variant of the EDI, the available water resource index (Han and Byun, 2006), in order to compute the beginning and the end of the rainy period for each station. This could also show if this period is changing over time. It has been found that there was no change (Traore et al., 2000) in this region, but this does not support the local farmers' point of view (Roncoli et al., 2001)
Finally, a summary of all methods to fill the daily data gap is necessary and the development of a precise method is indeed very important.