2.10 WATER SYSTEMS MODELLING
2.12.3 Internal Variability
Climate models are aimed at simulating long-term average conditions of the atmosphere as a result of anthropogenic greenhouse emissions. However, the earth’s climate system is also influenced by short-term internal variability. These are caused by natural processes in the atmosphere and oceans. For instance, the phenomena such as the North Atlantic Oscillation (Hurrell et al., 2001; Wanner et al., 2001; Hurrell and Deser, 2009) and the Atlantic Meridional overturning circulation (Bingham et al., 2007; Cunningham et al., 2007; Kanzow et al., 2010) are known to cause significant fluctuations in precipitation over the high latitudes (Fowler and Kilsby, 2002; Folland et al., 2009). Researchers such as Genesio et al. (2011) and Wolff et al.
(2011) reported a link between the El-Nino/ Southern Oscillation and the inter-annual rainfall variations in Africa. This natural climate variability adds more uncertainty in the climate projection since it is not adequately represented in the current generation of the GCMs (Hulme et al., 2001). When quantifying the various sources of uncertainty in climate modelling, Hawkins and Sutton (2009) concluded that internal variability contributes a much smaller uncertainty than the other sources.
38 Figure 2.4 Emissions and concentrations of the greenhouse gases (equivalent of carbon
dioxide) projected by the four SRES emission scenarios (Source: USGCRP, 2009)
Figure 2.5Global mean temperature variations predicted under various emission scenarios (Source: McCarthy, 2001).
39 2.12.4 Downscaling Techniques
Because GCMs are designed for global scale simulations, they have spatial resolutions of hundreds of kilometres (Wilby et al., 2004) and are too coarse (Grotch and MacCracken, 1991;
Kidson and Thompson, 1998) to be used at regional and local scales for impact assessment such as hydrological and water resources. The outputs of the GCMs are normally downscaled to attain finer resolutions of tens of kilometres (Wilby et al., 2004), to extract local-scale information suitable for application with hydrological and other local impact assessment modelling tools. A choice of method for downscaling adds more uncertainty in the prediction of the impact of climate change because of limitations in the assumptions used in the methodologies.
Downscaling GCM data is normally achieved by using one of two techniques; dynamical or statistical approaches. A number of studies provide detailed reviews on the past and current developments and on the application of the two methods of downscaling (Hewitson and Crane, 1996; Wilby and Wigley, 1997; Zorita and von Storch, 1997; Xu, 1999; Wilby et al., 2004;
Hewitson and Crane, 2006; Fowler et al., 2007; Teutschbein et al., 2011). On the other hand, extensive research has focused on comparing and contrasting the general strengths and limitations of each of the two general downscaling techniques as well as quantifying their related uncertainties in the hydrological context (Kay et al., 2009; Prudhomme and Davies, 2009; Quintana-Segui et al., 2010; Chen et al., 2013).
Statistical Downscaling
The statistical (empirical) downscaling approach entails the use of any of several statistical techniques that map large-scale climate GCM ‘predictors’ and circulation characteristics to local or regional-scale meteorological ‘predictants’ (Xu, 1999; Wilby et al., 2004; Ghosh and Mujumdar, 2008). Some of the widely applied statistical methodologies include regression methods, weather pattern-based methods, stochastic weather generators, artificial neural networks, continuous vorticity methods and self-organising maps. Even though these methods are substantially different in approach, they are based on the common premise that regional climate is influenced by both large-scale climate and local physiographic features (Timbal et al., 2009) and as such, a statistical relationship can be derived based on observed historical data. The established relationships can then be used to predict future climate under different conditions indicated by a GCM.
Statistical downscaling considers several assumptions, including: i) there is a statistically significant relationship between large- and small-scale predictor variables; ii) the established
40 relationship is constant and does not vary under future climatic conditions; and iii) the predictor variables and their changes are well characterised by the GCM (Karl et al,. 1990; Wigley et al., 1990; Wilby and Wigley, 1997; Wilby and Wigley, 2000). These assumptions introduce inherent uncertainties in the projection of future climate conditions. Details of the various empirical methodologies have been widely documented in the contemporary literature and some can be found in Wilby and Wigley (1997), Wilby et al. (1998) and Hewitson and Crane (2006).
According to Gachon and Dibike (2007), some of the main advantages of the statistical downscaling approach are that it is a relatively easy method to apply; it is not computer intensive, and it provides site-specific, high resolution climatic information. Another advantage noted by Hessami et al. (2008) is that the technique performs well in regions with highly heterogeneous environments. However, the approach has a major drawback since the fundamental assumptions cannot be verified, raising concerns that the established present-day statistical relationships may not be valid in future (Wilby and Wigley, 2000).
Statistical downscaling techniques have been widely used for regional climatic and impact assessment and their use has also been promoted for the reasons mentioned above. Some studies such as Wilby et al. (2004) and Schmidli et al. (2006) provide detailed guidelines on using statistical downscaling to develop climate scenarios, while other studies have evaluated and compared the performance of various statistical techniques (Heyen et al., 1996; Huth, 1999; Busuioc et al., 2001; Khan et al., 2006; Maurer and Hidalgo, 2008).
According to Khan et al. (2006), uncertainty in statistical downscaling arises from the concepts on which the downscaling models are based, as well as from the historical climate data used in the process. Dibike et al. (2008) evaluated the implications of uncertainties related to statistical downscaling, remarking that such uncertainties make it “difficult to have great confidence” in the output climate variable for “meaningful climate change impacts studies”.
Contrary to that, Segui et al. (2010) indicated that uncertainties related to downscaling of GCM data are less important than uncertainties due to other sources, such as emission scenarios.
This conclusion was later supported by Chen et al. (2011) who added that the downscale- related uncertainties are relatively minor.
41 Dynamical Downscaling
The basic aim of dynamic downscaling is to extract high resolution local-scale climate information from coarse resolution large-scale GCM data (such as the lateral boundary conditions, sea surface temperature and initial land surface conditions). This is generally attained by the use of limited-area models (LAMs) or regional climate models (RCMs). These are capable of achieving spatial resolutions of about 0.50 (longitude, latitude) and can simulate regional climatic features including orographic precipitation and extreme climate events (Fowler et al., 2007). Though the dynamical downscaling approach has the disadvantage of being computationally expensive and is strongly dependant on the parent GCMs’ boundary conditions (Chen et al., 2011), the method is based on physically consistent atmospheric processes and is thus achieving reasonable simulations of the atmospheric flow at the regional scale (Caldwell et al., 2009).
There are several dynamical downscaling approaches and Xu (1999) (after Rummukainen, 1997) summarises some of the commonly used methods: i) running a regional climate model using data from a GCM as geographical or spectral boundary conditions, also known as ‘one way nesting’; ii) performing global scale experiments with high resolution atmospheric regional climate models (RCMs) with data from GCMs as initial and boundary conditions; and iii) using a variable-resolution global model where the highest resolution is over the area of interest.
Uncertainties in dynamical downscaling arise mainly from the imperfect structures of the RCM, as well as from the driving GCM outputs (Rowell, 2006). Such uncertainties can either be significant or minor, depending on scale, season and the climate variable under consideration.
For instance, according to Rowell (2006), the uncertainties in temperature simulations are less than those for precipitation. However, the uncertainties associated with the RCMs are less significant than those associated with the emission scenarios (Rowell, 2006). Déqué et al., (2007) suggest that the largest uncertainties in the RCMs climate outputs are due to uncertainties in the boundary conditions of the driving GCM rather than to the RCMs themselves.
Since many downscaling techniques have been developed, the choice of any particular one is critical when predicting climate change on local and regional scales and the results should be interpreted with great care (Chen et al. 2011). Concurrent application of several downscaling methods may yield a better understanding about the level of uncertainties relating to the downscaling approaches.
42 2.13 SUMMARY
Hydrological models are commonly used tools to simulate the different basins’ hydrological processes. The above sections highlighted the pertinent technical aspects relating to the various forms of the models, ranging from rather simplified to sophisticated structures. The choice and application of any single model for hydrological investigations depends on the amount of detail required, as well as the data available to inform and drive the model. It is therefore important to select a model which is the most appropriate with regard to structure and data availability. In general, hydrological modelling is subject to a variety of uncertainties, and as such there are several approaches developed to evaluate and assess these uncertainties.
Outputs of climate models constitutes an additional source of uncertainty in water resources projections under the influence of climate change, in which case climate projections are used to drive the hydrological models. It is important that all these technical issues be considered when estimating future status of water resources under climate change.
43 3 METHODS