A worked example: Ethiopia

Since the 1970s there has been renewed interest from the government and non-governmental organisations to use rainwater harvesting (RWH) for domestic use, crop production and livestock (Moges et al 2011). Ethiopian agriculture is heavily dependent on rainfall (Yousef and Asmamaw 2015). There is abundant rainfall but about 80% occurs between June and September (Yousef and Asamamaw 2015). So harvesting this would be ideal, but is it really the outcome hoped for?

Studies such as Lebel et al (2015) have claimed that RWH works in Ethiopia. For example, it has provided an extra 0.5m/ ha in irrigation and maize production has expanded at 6% per annum (Yousef and Asamamaw 2015). However, despite a significant number of studies claiming RWH in Ethiopia has the potential to increase crop yields and farm income, reality tells a different story.  During my reading I have come across literature that shines a different light on RWH in Ethiopia.

Studies have shown various issues with RWH processes carried out in Ethiopia. Moges et al (2011) review the performance of ex situ RWH systems in Ethiopia by amalgamating assessments from other literature. They conclude that the potential of RWH in Ethiopia is site specific. This relates to my previous post which argues that local scale modelling is better than continental scale modelling when trying to analyse the potential RWH has in an area. By saying it is site specific it is suggesting that modelling, to see if RWH would work, needs to be done according to the conditions in each site.

There were 5 aspects, occurring in all regions studied, that are associated with the unsuccessfulness of RWH in Ethiopia. These are planning and implementation, water availability, operation, maintenance and socio- economics and other issues (Moges et al 2011).  A better understanding in each of these fields is required to make it more successful and allow it to create higher crop production and farmer income.

In Ethiopia, Moges et al (2011) found that there was poor planning and implementation of the RWH systems. There is only one experimental site for testing and developing RWH systems in the whole country (Moges et al 2011), and this has led to a lack of education of the locals who therefore lack technical skills and expertise.  Furthermore, farmers have to achieve ambitious targets set by authorities (Segers et al 2008). As a consequence, targets are prioritised over the farmer’s needs. An example is the use of large ponds which take up lots of land space regardless of the land scarcity that each farmer has (Lemma2005).

Another factor preventing the success of RWH was a shortage of water available. The three main issues causing a lack of water was: small storage capacities, a high loss of water through seepage (Yousef and asmamaw 2015) and evaporation, and inefficient water application by users (Moges et al 2011). One of the main reasons for this was due to the lack of education and training for the locals and so a possibly way to overcome this is to educate the users better before implementing RWH systems. For similar reasons, there was a lack of maintenance of the systems. The locals were not trained to upkeep the infrastructure and there was a lack of clarity amongst the NGOs and institutions on who was responsible for maintenance and upkeep (Moges et al 2011).

Socio- economic impacts are issues such as the farmers having to change their farmed crop from cereals to high value crops. In addition, there was very little difference between the users and non- users of the RWH systems in terms of things like per capita expenditure and food self- sufficiency months (Haile (2008) from Moges et al (2011)). This can be seen as a positive in that the farmers who adopted RWH systems were not affected by their failures, however it also means that for all the extra time and finance invested into the systems, very few benefits were reaped. Finally, there were an array of other impacts across all regions. These include things like increased diseases such as malaria and drowning of children and animals (Moges et al 2011).

Aside from the 5 topics above, there has also been a lack of country wide assessments on the performance of RWH (Moges et al 2011). This means that one does not know how well the systems are working on a larger scale and affecting populations as a whole. Additionally, one is not aware of the problems experienced in one region and so these cannot be dealt with when investing in other RWH systems.

Therefore, there is a lot of evidence to suggest that RWH is not yet successful in Ethiopia. I still stand saying that RWH can be a viable solution to Africa’s water problem for agriculture, but further research and education needs to be put done for it to be beneficial to users, and the example of Ethiopia shows this. The analysis by Moges et al (2011) has general applicability and so similar studies can be carried out on other parts of Africa. RWH has been a huge success in some regions of Africa, but I used Ethiopia to show that it is not always the solution, and conditions unique to each locality need to be considered.

...Map it a little at a time!

The last post showed the study by Lebel et al (2015) which carried out modelling on a continental scale across Africa. As stated in the last post, I think modelling should be carried out on a smaller, more detailed scale to really capture the how well rainwater harvesting (RWH) works specific to each area. This post aims to shw that modelling RWH on a smaller scale and in more detail, can be more effective than when modelled at a continental scale.

Nthuni et al (2014) used spatial modelling techniques in ArcGIA to model the benefits of RWH in Kakamega, western Kenya. They created four conceptual models at three different levels of detail to show the potential of RWH as a source of safe water for domestic use. Geospatial data and GIS were used to map the areas where RWH has most potential. RWH is not commonly used here and so this study investigates the potential it has. The regional climate is characterised by heavy rainfall every afternoon, but climate change means there are more frequent and extended periods of drought. This needs to be taken into account when assessing RWH potential.

Modelling at this scale when determining the potential of RWH is, in my opinion, more effective than continental scale modelling for many reasons. Firstly, this model considers the rainwater received by the catchment over a given period of time (Nthuni et al 2014). This is more accurate than at a continental scale because climate can change considerably across such a large land mass like Africa. Furthermore, at this scale, one is able to assess the social factors impacting the potential of RWH in the area. For example it can look at how densely populated the region is and even the wealth of the region to see if they have the infrastructure for, or can afford, RWH (Nthuni et al 2014). At a continental scale this is not possible and so saying an area has high potential for RWH would be inaccurate since it would not have considered the socioeconomic factors of the local people. For example, in this study they assessed the potential RWH has with reference to demand from that area. They didn’t just look at how many people would use the water, but also the domestic use of each individual or household over a period of time (Nthuni et al 2014).

The land in Kakamega is uneven terrain. The study found that up to 16000m asl there is an increase in precipitation, but beyond this the relationship is inverted (Nthuni et al 2014). This is another factor important in determining RWH potential that would not be taken into account if using modelling at a continental scale. Another key conclusion from this study is that there are three sub locations within Kakamega that would not be suitable for RWH use. This is because these areas have the highest population densities within the region. Again, this is another factor what would not be considered by continental scale modelling. The detail when modelling at a continental scale is not fine enough to analyse population densities of areas in this much detail. Furthermore, this is an essential point to consider because in the light of climate change and growing populations it is likely this could become an issue for many areas of Africa and so needs to be taken into account. By looking at locations in this much detail one is able to tell if the potential of RWH is restricted by population densities being too high, a lack of sufficient rainfall, or a mixture of both.

Therefore, as we can see from this example, modelling in more detail than at a continental scale is much more effective. Continental scale modelling is not detailed enough to show many things that we can decipher from more detailed modelling at a local scale. The study by Nthuni et al (2014) looked at the potential of RWH for domestic purposes. This blog focuses on agriculture in Africa so the methods and concepts used in this study can be applied to the look at potential of RWH for agricultural purposes. Continental scale modelling may be appropriate to research other factors but when researching the potential of RWH I think local scale modelling is better.