Modelling Climate Change Impacts: Precipitation and Surface Water Supply

Greetings and Salutations to all the curious readers out there!

My last post attempted to describe the changes in the frequency and intensity of precipitation we’re likely to see with climate change over Africa. Leading on from that theme, I’m now going to assess the differences between two studies attempting to model and map changes in precipitation and subsequent water supply across Africa – de Wit and Stankiewicz (2006) and Faramarzi et al.,(2013). Faramarzi et al., (2013) models future climate projections (18 scenarios combined) from 5 global circulation models (GCMs) under the four IPCC emissions scenarios (IPCC, 2007) fed into an existing Soil and Water Assessment Tool (SWAT) (Arnold et al., 1998).

Precipitation

Figure 1a displays the mean expected changes in precipitation across Africa in the last quarter of the 21^st century (de Wit and Stankiewicz, 2006). We can compare these expected changes in precipitation with those put forward by Faramarzi et al. (2013) (Figure 1b).

The two precipitation projections show fairly similar results for Northern Africa, with large reductions in precipitation. This means the Northern segment of the continent - with countries such as Egypt, Libya and Algeria already experiencing hyper-arid conditions - will become even more stressed due to decreases in precipitation by 25-50%, according to Faramarzi et al.,2013. Furthermore, both models project large reductions in precipitation for south-west Southern Africa (albeit by a greater amount with Faramarzi et al.’s model (2013)), and modest increases in precipitation are projected by both models for central and Western Africa.

Figure 1a and 1b. 

However, the two models disagree on the fate of East Africa; whereas de Wit and Stankiewicz (2006) predict modest increases in precipitation in the East, Faramarzi et al.’s (2013) model predicts declines in precipitation of up to 25% in Ethiopia, Somalia and Kenya in the Horn of Africa. It is possible these differences could be related to the differing time periods assessed by the two models; whereas Faramarzi et al. (2013) models the future period 2020-2040, de Wit and Stankiewicz (2006) investigate the last 25 years of the 21^st century. Furthermore, the models have differing resolutions; Faramarzi et al. (2013) use the subbasin as the basic unit of assessment for projections, and as such the map goes into a higher level of detail – the de Wit and Stankiewicz model may have generalised the trends over East Africa meaning any reductions in precipitation were ignored. Despite these differences, the models predict changes in precipitation in completely different directions. This suggests that it may be down to the difficulties of, and uncertainties in, predicting precipitation in comparison to say, temperature.

Water supply

It is widely discussed in the literature that stream flow and blue water resources are sensitive to precipitation (Faramarzi et al., 2013); therefore I would expect changes in water supply to largely mirror changes in precipitation. In order to model the impacts that changes in precipitation will have on surface water supply, de Wit and Stankiewicz (2006) define 3 climatic regimes over Africa, which are separated by threshold precipitation values. All areas of Africa fall into one of these regimes. They go as follows:

·         The dry regime: Areas receiving less than 400 mm year^-1 which have no perennial (long-term!) surface water drainage. This regime currently covers the largest area over Africa (41%).

·         The “unstable” intermediate regime: Areas receiving between 400 – 1000 mm year^-1. Here, surface water drainage increases linearly with increasing precipitation. A change in precipitation would directly results to a change in surface water supply.

·         The ‘wet’ regime: Areas receiving over 1000 mm year^-1. Here, density decreases slightly with increasing rainfall.

Figure 2a is a graph showing the effect of the predicted 10% drop in precipitation on drainage density. The unstable intermediate regime shows the most dramatic drop in surface water supply: the exponential curve means that regions on the regime’s upper boundary (i.e. receiving 1000 mm year^-1) would experience a 17% reduction in drainage. On the other hand, regions on the lower end of the boundary, receiving 500 – 600 mm year^-1, the same drop in precipitation would cut surface water drainage by 50 – 30% respectively. This is particularly disturbing given Figure 2b – a map of Africa divided into the three surface water regimes - which shows how 75% of African countries fall at least partly into this regime, which covers approximately 25% of the continent. Most of Southern Africa falls into either this unstable regime or the dry one, and large sections receive their sole water supply from the Orange River, which has its sources in the unstable regime.

Figure 2a and 2b.

Given the reductions in precipitation projected for Southern Africa above by both models (reductions ranging from 10-50% across both models), much of this region is likely to experience significant losses of what little drainage it does already have. This is in agreement with several studies assessed by Niang et al.,(2014), which point to future decreases in water abundance for Southern and Northern Africa. Furthermore, despite the increases in precipitation predicted for East Africa by the IPCC (although this is not in agreement with Faramarziet al., (2013)), de Wit and Stankiewicz (2006) point out how with very small amounts of rainfall to begin with (as most of it falls within the dry or unstable regime), this may not significantly increase its surface water drainage density.

We can compare these predicted changes in surface water drainage to the graphs of projected changes in blue water supply put forward by Faramarzi et al. (2013) (Figure 3a and 3b). Blue water is defined as the water yield plus the deep aquifer recharge which are renewable resources (Falkenmark and Rockstrom,2006). Figure 3a shows the blue water recorded from 1975-1995, whereas Figure 3b displays the % difference of the 2020-2039 data period from the 1975-1995 period. Note how similar Figure 3a is to de Wit and Stankiewicz’s (2006) map of surface water supply (Figure 2b) – this would suggest the data is of high reliability.

Figure 3a and 3b.

Overall, Figure 3b is surprising as it displays increases in blue water of up to 400% in the southern and northern parts of the continent. This is in stark contrast to the reductions in precipitation predicted by Figures 1a and 1b, however Faramarzi et al. (2013) note that these increases may only be construed as so large because of the small amounts of blue water availability to begin with (as shown by Figure 2b). However, as expected, countries located in the Horn of Africa and the Sahel will suffer from decreases of blue water resources with range from 25-100%.

Conclusions and Thoughts

Overall, these two studies agree that we are likely to see decreases in precipitation occurring over Northern and Southern Africa during the 21^st century. Both models agree precipitation in Central Africa is likely to increase modestly, however their predictions for East Africa differ with Faramarzi et al. (2013) predicting a strong decrease in precipitation, and de Wit and Stankiwicz (2006) predicting an increase.

In terms of freshwater supply, de Wit and Stankiewicz predict a decrease in Southern and Northern Africa, whereas Faramarzi et al., (2013) project strong increases. The two models do however agree on likely decreases in East Africa.

It’s important to reflect on the fact that there is a growing body of literature arguing climate change in Africa will have an overall modest effect on future water scarcity relative to other drivers, such as population growth, agricultural growth and land use change (Niang et al., 2014). These studies could be criticised in that they do not consider any such changes land use changes, or the associated changes in soil parameters such as increases in soil erosion through deforestation. This in turn can affect partitioning of rainfall into runoff and infiltration (Faramarzi et al., 2013). Lastly, inadequate observational data throughout Africa remains a systematic limitation for estimating future freshwater availability (Niang et al., 2014).