Challenges of calculating dunefield mobility over the 21st century

Geomorphology 59 (2004) 197 – 213 www.elsevier.com/locate/geomorph

Challenges of calculating dunefield mobility over the 21st century Melanie Knight *, David S.G. Thomas, Giles F.S. Wiggs Department of Geography, University of Sheffield, UK Accepted 16 July 2003

Abstract Attention has been directed towards both the impacts of future climate change on environmental systems and dunefield activity in the past, but there has been relatively little consideration of potential dune mobility in a future and possibly warmer world. This paper considers the use and limitations of four Global Circulation Models (GCMs) (Hadcm3, Hadcm2, CSIROmk2b and CGCM1), in combination with simple dune mobility indices to predict the activity of the Kalahari dunefield. It is clear that uncertainties surround GCM resolution and accuracy, mobility index robustness for the calculation of intra-annual dune mobility and data collection for mobility index calibration. Macro-scale studies that look at large areas of the world over long time scales are well suited to GCM and mobility index use, but dune mobility can be variable within a dunefield, and it is the extreme sand transporting events, occurring at high temporal resolutions, that are the most important for short term studies. To investigate intra-annual changes in dune mobility over a specific dunefield techniques such as downscaling, weather generators and probability curve fitting can help provide climate predictions for smaller areas over shorter time frames. However, these methods introduce uncertainty of their own, and they often rely on the accuracy of original GCM predictions or the climate parameter relationships observed at present. Analysis of intra-annual changes also requires mobility indices that can model monthly mobility patterns well, although existing indices have only been used for calculating annual dune mobility potential. When they are used for intra-annual predictions, the lack of lag response between precipitation decreases and the assumed vegetation dieback leads to an exaggerated amplitude pattern of dune mobility throughout the year. Calibration of dune mobility indices to dune mobility observed on the ground is therefore important but is hampered by a lack of observed measurements for individual months. Solutions are available to overcome some of the outlined problems, but they can provide their own set of uncertainties, which combine to further reduce the confidence given to future dune mobility predictions. D 2003 Elsevier B.V. All rights reserved. Keywords: GCMs; Dune mobility; Climate change; Kalahari

1. Introduction Mobile dunes can be an aeolian hazard that affects transport networks, agricultural productivity, water supplies and residential areas. In Kuwait, sand covers

* Corresponding author. Fax: +44-114-279-7912. E-mail address: [email protected] (M. Knight). 0169-555X/$ - see front matter D 2003 Elsevier B.V. All rights reserved. doi:10.1016/j.geomorph.2003.07.017

roads to and from oil fields (Khalaf and Al-Ajmi, 1993) and freight movements across the Gobi Desert, in China, are disrupted when dunes inundate rail lines (Dong et al., 2000). Agricultural land on desert margins can become vulnerable during droughts when the protective vegetation cover dwindles to allow sediment transport. Kumar and Bhandari (1993) noted this as cultivation expanded into the Rajisthan Desert in India leading to a positive feedback of vegetation

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destruction, increased dune mobility and exacerbated surface instability. Water supplies also experience problems as planting schemes are required to stabilise the ground near to areas such as the Al-Hasi wadi in Saudi Arabia (Goudie, 1990). Dune mobility can therefore affect commercial interests and people’s livelihoods, and although dunefields are prevalent in both high and low income nations, it is the lesser developed countries with the least capacity to adapt that could suffer the most financial, social and developmental consequences. The magnitude of the problem is greater when a collection of dunes experience instability and erosion. It is therefore important to study dunefield mobility across the globe and how this may change in the future. Throughout the world, dunefields experience differing levels of activity or mobility. These can be categorised as: active, where dunes migrate; dormant, including areas with reduced aeolian activity resulting from a change in sediment supply, wind regime, vegetation cover or moisture content; and relict, where dunes are very stable often with a high proportion of vegetation cover (Tchakerian, 1999). High-wind-energy environments such as the Rub’ al-Khali in Saudi Arabia are composed of very mobile bedforms whilst lower-energy environments like the Mega Kalahari in southern Africa and most of the Australian dunefields are relatively stable with any movement restricted to the crests of the dunes. However, even those in the latter category have experienced episodic movement within the recent past when periods of decreased rainfall have promoted increased dune mobility. Mabbutt (1969) noted that the so called ‘‘stability’’ of the Australian dunefields should not be taken for granted as the droughts of the 1950s and 1960s led to unusual sand movement on the dune flanks and plinths. In the United States, accounts from early explorers in the 19th century show that dune activity was apparent from north Nebraska to southern Texas and from the Chihuahuan Desert to the Great Plains (Muhs and Holliday, 1995), an area covering northern Mexico and at least six states of the southern and central part of the USA. This occurred during a time when there was a lower precipitation to potential evaporation ratio to create lower moisture conditions. Optical dating techniques reveal that there were also periods of parabolic dune movement in the Great Sand Hills of Saskatchewan, Canada, during severe droughts in the

1700s (Wolfe, 2001). Likewise, in the 1980s, high levels of mobility occurred in the southwest Kalahari when precipitation was only 50% of that normal for the period 1960 –1990 (Bullard et al., 1997). Droughts are set to become the norm of a warmer world, and so, mobility levels observed during these extreme events could become the average conditions of the next century. Southern Africa has been getting warmer. The 1980s were 1 jC warmer than the start of the century and variability of precipitation has increased with the 1990s receiving 20% less rainfall than that of the wetter 1970s (Hulme, 1996). Over the coming centuries, Bridgman (1998) suggests a further 20% reduction in precipitation with the potential for an increase in drought episodes. Wind speed is much more difficult to predict, but Wasson (1983) states that the wind regimes are dependent on the seasonal movement of the Inter Tropical Convergence Zone (ITCZ), which is set to change in the future, too. Dunefields such as the Kalahari, the Australian deserts and the north American environments have the potential to shift from their currently dormant state to become more active systems, but there are questions of if, when and to what magnitude these changes are going to take place. Previous work predicting dune response to climate change has focused upon the North American dunefields in both the USA and Canada. Historical analogues have been used where the future mean climate is assumed to be like that of a past drought event (Rosenzweig and Hillel, 1993; Wolfe, 1997; Dong et al., 1997). However, these analogues used on their own are not always considered to be good indicators of what could happen under a contemporary increase in atmospheric carbon dioxide or conditions relevant to a global warming phenomenon and so other researchers have used physically based climate models such as Global Circulation Models (GCMs) to predict variables such as precipitation, potential evaporation and temperature over the next century (Muhs and Maat, 1993; Stetler and Gaylord, 1996). Despite these attempts, GCMs as a source of climate data have not been fully exploited to predict large-scale dunefield responses, across the globe. Although Blumberg and Greeley (1996) adopted a GCM approach, their work successfully modelled present dunefield conditions, but no runs were completed for future activity states. Muhs and Maat (1993) integrated present-day wind

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speed data with predictions from only two GCMs for precipitation and potential evaporation and Stetler and Gaylord (1996) created a regional climate model that was limited for use in their small field site in Washington. In addition to the limited geographical extent of research and the scarcity of GCM integration, only basic climate indices have been used to model dunefields. The number of physically based dunefield models is small and mostly restricted to quantifying transverse dune systems (Bishop et al., 2002; Momiji, 2000; Lima et al., 2002). They are in their early stages of development and have at times produced geomorphology different to that observed (Bishop et al., 2002). Therefore, most attempts at modelling dunefield mobility have used simple climate indices such as Talbot’s (1984) and Lancaster’s (1988) that categorise dune activity as fully active, flank instability, crest movement only and complete inactivity. These require general data, such as precipitation, potential evaporation and wind speed measurements and have shown a good relationship with mean annual rates of sand transport in the USA (Lancaster and Helm, 2000). They were originally designed to reconstruct palaeo-dunefield activity and predictions are usually provided for a year-by-year basis. However, there may be a change in the timing of peak dune mobility throughout the year, requiring calculations to be made for each calendar month or at the very least on a seasonal basis.

2. Aims and study area The purpose of the following discussion is to assess the uncertainty surrounding possible predictions of future dunefield activity. The first part of the paper highlights some of the challenges of prediction using existing dune mobility indices with climate predictions from GCMs. In the latter part of the discussion, suggestions are then given for how a few of these limitations can be overcome. Whilst the issues highlighted in this paper are relevant to many dunefields, the analysis here focuses on the Kalahari dunefield in southern Africa. This is because it is one of the largest sand seas in the world, covering an area 2.5 million km2 (Scholes et al., 2002), it incorporates areas that are both active and dormant in terms of dune mobility and because a lot

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of research has already been carried out into the contemporary climate – sand transport relationships of the area (Bullard, 1994).

3. Methods and challenges Quantitative assessments about future climates gained from the Intergovernmental Panel on Climate Change Data Distribution Centre (IPCC-DC) can be accessed freely for a limited number of climate models. Four GCMs from three different modelling groups are presented here: Hadcm3, Hadcm2, CSIROmk2b and CGCM1 (Table 1). They were chosen for several reasons: They span a range of ages from the oldest CGCM1 to the newest Hadcm3; they provide the most spatial detail because of their relatively fine grid resolutions; and they offer several magnitudes of warming to produce the least and worst-case scenarios. Data are also available for the important climate variables of temperature, precipitation and wind speed, and several time slices exist with GCMs providing values for each calendar month. Observed climate data for the period 1960 –2000 were gained from either the five meteorological stations situated in the southwest Kalahari (Fig. 1) or the gridded CRU data set with a 0.5j
0.5j resolution. This is further described by New et al. (1999). Presently, discrete models are not available for linear dunefield modelling so the two mobility indices of Lancaster (1988) (Eq. (1)) and Talbot (1984) (Eq. (2)) were adopted. They have previously been used in the Indian Thar Desert (Kar, 1993), the Sahel (Talbot, 1984), the USA (Lancaster and Helm, 2000) and the

Table 1 The grid resolutions and sensitivities of four GCMs GCM

British Hadcm2 and Hadcm3 Canadian CGCM1 Australian CSIRO-mk2b

Grid cell resolution, longitude and latitude

Approximate area of cell, km2

Sensitivity

3.75j
2.5j

93,750

Low

3.75j
3.75j 5.625j
3.214j

140,625 180,723

Mid High

Sensitivity relates to the amount of warming considered for the 21st century with low being the least change from present conditions.

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Fig. 1. Southern Africa with (a) the coarse grid resolution of the CSIRO-mk2b model and (b) the relatively finer resolution of the Hadley models. The shaded area is a rough outline of the Mega Kalahari dunefield (taken from Thomas and Shaw, 1991), and the points are the location of the five meteorological stations used.

Kalahari (Lancaster, 1988; Bullard et al., 1997), suggesting that there is much more experience in using them than most of the other regression models described by Tchakerian (1999). The Talbot index was used when the M value of the Lancaster method could not be calculated. The Lancaster (1988) Index: M ¼ W =ðP :PEÞ

ð1Þ

where M is the mobility value, W is the percentage of time the wind is above the threshold for sand transport, P is the precipitation value (mm/month) and PE is the potential evaporation (mm/month). Dunes are categorised as inactive when M values are below 50, crest active when values lie between 50 and 100, flank active when M values are between 100 and 200 and fully active when M values exceed 200. The Talbot (1984) Index: M ¼ V 3 =Mo2 where Mo ¼

12 X

115

ðPÞ1:111 T  10

ð2Þ

where V is the mean wind speed (mph), Mo is the moisture index, P is the precipitation value (in./

month) and T is the monthly temperature (jF/month). Dunes are described as stable when M values are below 1, episodically active when M values are between 5 and 10 and fully mobile when M values are above 10. The Fryberger (1979) technique is used to calculate potential sediment transport because it is effective at emphasising the importance of winds that exceed the threshold for sediment transport (Eq. (3)). Although the technique only considers the erosivity of the dunefield environment, it is useful for investigating the importance of extreme events: DP ¼ V 2 ðV  Vt Þt

ð3Þ

where DP is the drift potential, V 2(V  Vt)t is the weighting factor and t is the percentage of time the wind blew for that category. For each meteorological station, 1 year was randomly chosen and the DP values were compared to those gained from the equation where only the raw monthly average was used. This simply compares two techniques where higher daily wind speeds are weighted to be more important against a method where only the monthly average is considered. Several challenges have arisen from this methodology including: what GCM results provide for those

M. Knight et al. / Geomorphology 59 (2004) 197–213 Table 2 Challenges of predicting future dune mobility using climate predictions made by GCMs and simple dune mobility indices Challenges GCM climate output Coarse spatial resolution of output for all GCMs (finest is 2.5j latitude
3.5j longitude) Data integration problems with impact assessment models (mobility indices) Lack of natural inter-annual variability in climate predictions made for the next 100 years Uncertainty surrounding GCM performance and accuracy Dune mobility indices Omit some of the factors that control mobility Zero precipitation yields high mobility values and there are no lag times considered Output describes only one of four activity states to provide nominal data Calibration of monthly mobility values Lack of ground truth data or measurements of dune mobility for each calendar month Difference between point measured and areal modelled data High values do not fit into the original mobility categories

assessing future dune mobility; what data are available for dune mobility index calibration; and how Eqs. (1) and (2) represent intra-annual dune activity. Table 2 lists these main areas of uncertainty and the subsets of problems that are now discussed.

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4. GCM outputs 4.1. Coarse spatial resolution A problem commonly cited by GCM users during impact assessments is the difference between the coarse scale at which the GCMs provide any output and the finer resolutions that are often required to describe the environmental system being studied (Joubert and Hewitson, 1997; Wilby et al., 1999; Russo and Zack, 1997). Only a single value is provided for each GCM grid cell, which means one precipitation, temperature or wind speed value for an area covering approximately 100,000 km2 (Fig. 1). Although temperatures can remain homogeneous across large dryland areas, precipitation is very variable and events can vary in magnitude over a few kilometres rather than the thousands of kilometres covered by a GCM. For example, within a CSIRO-mk2b grid cell, annual precipitation varies from less than 15 mm on the Namibian coast to 89 mm at Aus further inland (Lancaster, 1989). This has an influence on dune mobility calculations as one M value prediction covers an area where several states of activity may actually be occurring on the ground. An investigation of records from five meteorological stations, over a 20-year period, shows that dune

Fig. 2. Dune surface activity calculated from the Lancaster Mobility Index for each of the five meteorological stations located within the southwest Kalahari. Averages have been taken over a 20-year record, from 1980 to 2000, for each calendar month.

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mobility in the southwest Kalahari is indeed variable at a subgrid scale (Fig. 2). Each station has different mobility values and some experience activity on the crests whilst others remain stable. Three of these stations appear together in one GCM grid cell, even when the relatively fine resolution Hadley models are considered. This sub-cell variability is increasingly hidden the larger the grid cells used. For example, a larger proportion of the southwest Kalahari appears to have potential dune mobility using a grid cell that is 0.5j
0.5j compared to the smaller area produced when the cell size is increased to 1j
1j (Fig. 3). Within the coarsest CSIRO-sized cell, mobility can be as little as 0.06% of the cell average or nearly five times greater if mobility is calculated using data from a 0.5j
0.5j grid nested within the CSIRO one (Table 3). This could result in a CSIRO-sized grid predicting crest activity even when stability and flank mobility occurs in different parts of an individual cell. Further to this problem, Joubert and Hewitson (1997) suggest several of these cell values should be averaged together because there is little confidence in the climate predictions associated with a single cell. This would improve the accuracy of climate predictions over a large area but would also compound the spatial detail problem, as mobility values would be smoothed to an even greater extent. The greater the grid cell size or the

Table 3 Variance of dune mobility within a CSIRO-mk2b GCM grid cell Grid size, latitude and longitude

Minimum M value as a % of CSIRO cell

Maximum M value as a % of CSIRO cell

0.5j
0.5j 1j
1j 2j
2j 4j
4j

0.06 0.10 0.43 –

460 391 363 125

CSIRO-mk2b is considered 100%, whilst the percentages given above are what are found within the smaller-sized grid cells. M is dune mobility value.

coarser the GCM resolution the more spatial variability is removed during mobility calculations even in a relatively homogeneous desert such as the Kalahari. 4.2. Coarse temporal resolution Extreme events or periods of wind that greatly exceed the threshold for sediment transport are important to dunefield studies as these initiate the most sand transporting opportunities. For example, in the Namib Desert, 80% of sand is transported by winds blowing for only 6% of the time (Cooke et al., 1993). In the Kalahari, mean monthly wind speeds rarely reach the threshold for sand transport, suggesting extreme events initiate what erosion there is

Fig. 3. Dune surface activity calculated (a) using grids with a resolution of 0.5j
0.5j latitude and longitude and (b) with a resolution of 1j
1j latitude and longitude. The lighter shaded squares show where there is a potential for mobility whilst the dark squares show inactivity. The shaded area is a rough outline of the Mega Kalahari dunefield (taken from Thomas and Shaw, 1991).

M. Knight et al. / Geomorphology 59 (2004) 197–213 Table 4 Comparison of annual mean wind speeds to the threshold wind speed required for sediment transport for five meteorological stations located in the southwest Kalahari, South Africa Station

Mean, m/s

Threshold for movement, m s 1

Kuruman Twee Rivieren Upington Van Zylsrus Keetmanshoop

2.41 1.60 3.42 2.24 3.03

5.975 5.601 6.067 5.975 6.048

Thresholds were taken from Bullard (1994).

(Table 4). In the Kalahari dunefield, wind speed variability is high during the austral spring as winds may be absent some days and blow at rates over 6 m s 1 on others (Fig. 4). Unfortunately, GCMs only provide mean monthly wind speeds with no indication of variability within that month. This could potentially lead to an underestimation of dune activity. To illustrate sand transport disparities between those calculated using the monthly mean and those calculated using the full range of daily wind speed values, the Fryberger Drift Potential (DP) was calculated for each month (Fryberger, 1979). In Kuruman, not only did the magnitude of sand transport vary but so did the timing of peak events. For example, the average DP is higher during the early part of the year, in April, but underestimates movement during the windiest months from June to September. This is

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when wind speed variability is greater with more days exceeding the threshold for sand transport (Fig. 5). As expected, the mean smoothes out much of the variability observed within a year. 4.3. GCM accuracy GCMs are the only tools available that can consistently predict changes in climate due to increases in carbon dioxide, for the entire world and for a large number of climate variables (Smith and Hulme, 1998). However, postdictions made for regional climates over recent decades are often inaccurate, and much attention has been given to trying to quantify the uncertainty that surrounds such predictions (Webster and Sokolov, 2002). Presently, the degree of certainty can be grouped into four wide bands including: virtually certain, where trends occur on a global scale; very probable; probable, such as continental trends; and uncertain, which relates to regional changes (Barron, 1995). Not a great deal of confidence can be given to specific GCM results at fine spatial and temporal resolutions, and many have ignored predictions because the uncertainty appears too great. The Atmospheric Model Intercomparison Project (AMIP) has compared outputs from several GCMs describing the climate of southern Africa. It found that convective rainfall was poorly simulated by all GCMs, and performance appeared to be seasonally

Fig. 4. The average wind speed for September compared to the highly variable wind speeds that are measured three times a day. Values are taken from the Twee Rivieren meteorological station for the year 1982.

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Fig. 5. Drift potential values for the Kuruman meteorological station in 1990. Drift potential is calculated using the Fryberger (1979) method, and average potential is calculated using the percentage of time that the wind blows in the 0 – 4 m/s category, which is where the average wind speed fits in.

dependent (Joubert, 1997). To investigate the integrity of GCM results specifically for the Kalahari, the four GCMs described above were compared to the CRU observed data set with temperature being the most consistently modelled parameter yielding high regression relationships for all GCMs (Fig. 6). Precipitation was much more erratically simulated with the finer-

resolution Hadley models performing the best, although simulated values were still 25% more than those observed (Fig. 7). However, GCM output for both temperature and precipitation was within the subgrid variability shown in the observed meteorological record, a test of GCM validity used by Portman et al. (1992). Wind speed was poorly replicated,

Fig. 6. Comparison of the CRU observed temperature (jC) values with those postdicted by the four GCMs for the period 1961 – 1990. Values consider most of the southern African continent including the mega Kalahari. The R2 value is a correlation for all four GCMs combined, against the observed values.

M. Knight et al. / Geomorphology 59 (2004) 197–213

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Fig. 7. Comparison of the CRU observed precipitation (mm/day) values with those simulated by the four GCMs for the period 1961 – 1990. Values consider most of the southern African continent including the mega Kalahari. The R2 value is a correlation for all four GCMs combined, against the observed values.

with the Hadley models greatly overestimating the wind speed measured from 1961 to 1990 (Fig. 8). CGCM1 was the best at simulating this variable as the difference between the predicted and observed was approximately 10%. This compared to the 50% overestimation by both Hadcm2 and Hadcm3. Despite all

of these differences between the simulated and the observed, the Kalahari is relatively better represented than other wetter coastal areas and the east of the southern African subcontinent. Therefore, GCM predictions provide relatively accurate results for dune mobility studies where conditions are predictably arid.

5. The simplicity of dune mobility indices

Fig. 8. Comparison of the CRU observed wind speed (m/s) values with those simulated by the four GCMs for the period 1961 – 1990. Values consider most of the southern African continent including the mega Kalahari. The R2 value is a correlation for all four GCMs combined, against the observed values.

Several factors affect sand dune mobility including wind power (Tsoar and Illenberger, 1998), vegetation cover (Muhs and Maat, 1993), crusting (Karnielli, 1997) and land use. Yet, these parameters are crudely described by mobility models and are represented only by wind power and soil moisture variables. The Talbot model expresses wind power as the cube of mean wind speed and can be calculated using GCM output, but the percentage of time the wind is above the threshold for sand transport, required for the Lancaster model, is unavailable directly from GCMs. The mobility models consider vegetation cover using a proxy measure of the ratio of precipitation to potential evaporation and the Thornthwaite moisture index. These do not, however, consider the intensity and frequency of rain events. This is important because the high infiltration capacity of sand

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means optimal vegetation growth occurs when there is low intensity but highly frequent precipitation (Tsoar and Illenberger, 1998). There is also no way to incorporate the carbon fertilisation effect described by Le Houerou (1992) that could encourage greater vegetation cover due to increased future atmospheric carbon levels. For example, work carried out in the Mediterranean found that the decrease in precipitation and the increase in temperature over the last century led to a decline of 3% in plant productivity, but when this was coupled with an increase in carbon dioxide, productivity increased by nearly 30% (Osborne et al., 2000). The type of vegetation present also influences dune mobility. Grass cover provides more overall protection than sparsely distributed shrubs, yet, there is no consideration of this factor within the mobility indices. This is despite the scenario that vegetation species composition could alter in the future (Kremer et al., 1996). 5.1. Lag influences on dune mobility calculations The mobility models handle low precipitation levels poorly, resulting in infinite levels of mobility when zero precipitation values are given. In addition, no lag influences are modelled, which leads to dune mobility closely mirroring precipitation trends throughout the year (Fig. 9). For a single month, mobility can be

calculated as being high, yet, for the following month, complete stability may be predicted, which is a situation that rarely occurs in reality. Vegetation takes time to respond to changes in climate parameters even in dryland systems that are frequently cited as having a ‘‘pulse activity response’’ regime (Wand, 1999). Van Rooyen and Van Rooyen (1998) noted that several consecutive months of diminished rainfall were necessary to cause a decline in plant productivity between 1995 and 1996. A 12-month period of reduced precipitation was also required for Kalahari plant mortality in 1992 (Milton and Dean, 2000). In addition to the monthly lag times required for dune mobility prediction, there may also be a lag response between the onset of climate change, the impact on vegetation growth and dune erodibility. Different species may have different response times leading to a scenario where savanna grasslands react more quickly than shrubby woodlands (Overpeck et al., 1992). This is difficult to incorporate into climate change studies, and, often, it is assumed that climate change will instantly lead to a response in the systems being studied (Root and Schneider, 1995). 5.2. Mobility index calibration It is often not possible to apply a dune mobility index that was used at one scale to another, without

Fig. 9. The average precipitation and dune surface activity values for one decade in the southwest Kalahari. Surface activity is calculated using the Lancaster Mobility Index (M).

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recalibration (Refsgaard et al., 1999). When the mobility models are used to calculate monthly values the output does not fit into the original mobility categories. Therefore, there is a need to compare mobility index output with that observed for each calendar month. However, investigations are restricted to a piecemeal operation that relies on various sources of proxy data to try to infer sand dune mobility. Contemporary monthly dune mobility records are required but often re‘cords are only published describing annual rates of movement or what happens after extreme events. There may be a paucity of observed dunefield activity for specific months, but the erodibility part of the index, or the expression that describes soil moisture and therefore vegetation, can be calibrated to mobility index predictions. Fractional vegetation cover can be estimated from images containing information about the Normalised Difference Vegetation Index (Carlson and Ripley, 1997). These types of images have been used extensively in southern Africa (Di et al., 1994; Richard and Poccard, 1998; Van Rooyen and Van Rooyen, 1998) and help to provide information on vegetation cover for individual months. This areal information provided on a gridded basis can be combined with the point data describing dune mobility from qualitative accounts (Milton and Dean, 2000), aerial photographs (Leason, 1996) and erosion pin measurements (Wiggs et al., 1995). However, these provide only a general impression of regional trends on dune movement for individual months, which are specific to the Mega Kalahari.

6. Discussion This second part of the paper investigates the impacts that the limitations described above have on future dune mobility predictions. Possible solutions to the limitations are also discussed. 6.1. GCM spatial issues Predictions from GCMs are ideal for global studies as coverage is consistent worldwide and provides a good database for macro-scale projects. However, as subgrid processes are not considered, GCM end users need to focus upon continental-

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sized areas that are spatially homogenous in how they behave. For example, the Australian dunefields are predominantly stable with only localised sand movement in Coopers flood plain in Queensland (Mabbutt, 1969) and New Moomba in the Strezelecki (Tseo, 1990), but in dunefields that are closer to the threshold of activity, the large grid cells may be too coarse. In general, most of the Mega Kalahari is stable, but there are pockets of activity that occur at the subgrid level that will be hidden when using GCMs. There are several ways to downscale GCM results to produce regional predictions at finer resolutions, and these include: regression, weather generators, Limited Area Models (LAMS) and weather pattern approaches (Wilby and Wigley, 1997). A few of these methods have been compared by Wilby et al. (1998), revealing that different techniques produce very different results. For example, when predicting precipitation, some downscaled results yielded a positive increase of just 5% whilst others predicted a larger increase of 20%. Not only is the magnitude of change variable but also performance varies through space, too (Rasmus and Benestadt, 1997), making some techniques appropriate in some places but not in others. Many GCMs assume the linearity of relationships between continental weather patterns and local phenomena. However, many of southern Africa’s local weather systems are non linear to global patterns (Landman et al., 2001). Often, downscaling still relies upon GCM output so results are only as accurate as the initial GCM data and downscaling may lead to compounded error (Mearns et al., 2001). Little work has been published concerning downscaling techniques applied to the Mega Kalahari or other dunefield environments although the Climate Systems Analysis Group (CSAG) based in the University of Cape Town is working on a regional circulation model appropriate for those wishing to conduct regional investigations in southern Africa. Presently, research predicting Kalahari dunefield mobility is restricted to raw GCM data until these other projects are finalised. 6.2. Temporal issues Future climate variability proves to be difficult to predict so studies often work on 30-year time slices.

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The advantage of this approach is that uncertainties surrounding events such as the El Nin˜o Southern Oscillation (ENSO) and volcanic eruptions are smoothed and concealed. ENSO is poorly modelled by many GCMs. It occurs at a 3- to 4-year cycle, and so, any time slices shorter than this are heavily influenced by the inaccuracies created from simulating this event (Latif et al., 2001; Gates et al., 1996). In addition, other phenomena such as volcanic eruptions can cool global climate over 2– 3 years, and changes in sunspot activity can alter some areas of the climate on an annual basis. Thirty-year averages only provide three temporal snap shots of the dunefield environment over the next 100 years, but studies of dunefield activity have noted a decadal cycle in dune mobility trends in the southwest Kalahari (Bullard, 1994). Therefore, there appears to be a gap between what is ideal to describe the dunefield environment, decadal data, and that which is most robustly provided by circulation models. An increase in temporal resolution can provide increased detail, but not necessarily increased accuracy. Wind speed is crucial to dunefield mobility calculation, particularly the highly variable extremes that have an instantaneous impact on surface activity. These are not easily predicted by GCMs as even confidence in the mean predictions is low, but there are several levels of statistical analysis that can be performed involving both weather generators and probability distributions. Weather generators are not predictive tools in themselves, but they produce a series of random future climate scenarios constrained only by the specified mean and variance provided by an end user (Semenov and Barrow, 1997). These allow variability to be included into runs of future climates for areas where GCM data are at an unsuitable spatial and temporal resolution (Wilks and Wilby, 1999). However, Mavromatis and Hansen (2001) warn that generators often underestimate the actual variability that they are designed to predict, there are discrepancies between the different types of generator outcomes and there is the need for long historical records to help ascertain what present day variability is like. It is this last limitation that affects the Kalahari system as wind speed is notoriously difficult to measure consistently and long enough records may not be available for most of the dunefield environments of

the World. Dunefield predictions may therefore need to rely on averages with the user estimating what conditions are like during events such as ENSO. Frequency distribution curves such as the Weibull distribution may be simpler to adopt. This has been used in a range of environments to help site wind farms (Sulaiman et al., 2002) and choose the appropriate forestry techniques for extremely windy sites (Quine, 2000). The Weibull is currently the best probability distribution function to describe the power of the wind regime (Seguro and Lambert, 2000) and can help determine the probability and frequency of winds high enough to transport sediment and promote dune mobility. If the shape of the distribution curve is determined for the present wind regime, it may be modified using the mean future wind values to create new probabilities of wind speeds. This would allow GCM data to be used to calculate erosivity within the Lancaster Mobility Index. However, there is a limitation in that the present distribution curve may not hold true for the future environment. Variability may alter in addition to the mean (Meehl et al., 2000). 6.3. GCM accuracy Confidence has increased in GCM results with the new generation of models. Better parameterisation of oceans and land atmosphere interactions have led to improvements in simulating observed temperature trends. For example, the successor of CGCM1 yields more symmetric warming between the northern and southern hemispheres, which is in agreement with that measured (Flato and Boer, 2001). Even ENSO events are modelled more realistically as Hadcm3 simulates the amplitude of past events in a relatively more robust manner than seen with Hadcm2 (Collins, 2000). Other improvements of Hadcm3 include an improvement in the treatment of radiation, more realistic tropical winds and a better simulation of winter continental temperatures (Pope et al., 2000). However, in specific places or with some climate variables, both generations of Hadley models calculate very similar outputs. Giorgi (2002) describe variability measures in both Hadcm2 and Hadcm3 as being of the same quality and Arnell (1999) found that Hadcm3

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scenarios produce changes in runoff that are very similar to those produced by Hadcm2. It also appears that whilst some of the problems encountered using older models have been solved, new inconsistencies have emerged. When comparing postdictions of climate conditions to observed data for 1961 –1990, in the Kalahari, the Hadcm3 model was not necessarily any better than its predecessor. For example, the differences between the predicted and the observed precipitation values increased from approximately 5% using Hadcm2 to over 50% using Hadcm3 (Fig. 10). Similar increases in

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the differences between temperatures were also observed in the southwest Kalahari. Carter et al. (1994) warn that GCM outputs should not be treated as true predictions but as broad ideas of what possible future conditions may be like. With this in mind, many studies have used a combination of GCMs with other prediction techniques. The United States Country Program adopted three GCMs that simulated past climate well within their study site along with a synthetic approach where precipitation and temperature were arbitrarily increased by several percent or a few degrees. This combination of techni-

Fig. 10. The percentage difference between the simulated precipitation and temperature values and those measured in the CRU data set for the period 1961 – 1990. (a) Hadcm2 temperature, (b) Hadcm2 precipitation, (c) Hadcm3 temperature and (d) Hadcm3 precipitation. The positive values suggest an overestimation by the GCMs and the negative values suggest an underestimation.

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ques helped to capture the full range of potential scenarios (Smith and Hulme, 1998). Likewise, for the Kalahari, no single model is perfect, and so, a combination should be adopted when predicting future dune mobility. 6.4. Dune mobility indices The Lancaster and Talbot mobility indices are ideal for coarse-scale continental studies rather than those looking at regional patterns or individual dunes. The input data needed for their calculation are relatively easy to obtain and their simplicity allows them to be appropriate to many different dunefield environments. Their limitations become apparent if they are used for specific regions or at finer temporal resolutions, as sand movement may be influenced by controls not considered. The vegetation component of the indices appears to be the weakest part of the equation when trying to calculate monthly dune mobility values. Instead of relying upon raw precipitation data, it was suggested that a cumulative effect could be introduced by incorporating drought indices into the mobility calculations (N. Lancaster, personal communication). These would highlight when droughts or severe water shortages would occur and periods when vegetation would be at its lowest cover or productivity. Hayes (2001) lists several of these drought indices used both in America and in Australia including: percent of normal, the standard precipitation index; the Palmer Drought Severity Index (PDSI), a crop moisture index; and the surface water supply index. The PDSI is used by many US government agencies who rely on it to trigger drought relief programs. It is also ideal for dune mobility predictions because values lag several months after an emerging drought (Kipfmueller and Swetnam, 2000). Despite the initial appropriateness of this method, the PDSI technique has several drawbacks. It requires a long run of precipitation values in order to determine what is the norm and what constitutes a drought for each region, which may not always be available in drylands. The drought classes provided are also arbitrarily defined and were originally produced for the American states of Iowa and Kansas (Dalezios et al., 2000). The time taken to calculate just one part of the mobility index may be better directed to producing a more comprehensive

model that models vegetation directly rather than using the proxy of soil moisture. Vegetation models exclusively for the Kalahari Desert are currently being developed as part of the South Africa department for Arts, Culture, Science and Development project (I. Woodward and Drew, personal communication). More physically based models would be appropriate for future scenarios as they could incorporate a lot more about changing plant dynamics. However, data input requirements may be too sophisticated considering what GCMs are presently able to provide.

7. Conclusions: using GCM and mobility index data for predicting dune activity Predicting dunefield activity presents a number of challenges concerning both the use of GCM climate predictions and the use of mobility indices. Uncertainty surrounds the spatial and temporal resolution of the GCMs along with confidence in their results and the ability of mobility indices to provide results on a monthly basis. The coarse grids of the GCMs are appropriate for global studies but not for regional investigations where inter-grid processes are important. The Mega Kalahari is a large enough area to be covered by a number of the coarsest GCM grid cells, but there are areas of variability were downscaling would be useful. The poor temporal resolution of accurate GCM results also mean that studies requiring means for 30-year time slices are well provided for. However, dunefield mobility calculations benefit from variability data both inter and intra-annually, which are not so readily available from GCM simulations. The dune mobility indices themselves are simple, considering only wind power and soil moisture. These are ideal to incorporate with GCM data because input data are easy to obtain. The lack of lag effects is a problem because mobility mirrors the precipitation trend too closely ignoring observations that vegetation dieback is a result of a prolonged drought event. Few data are available to calibrate a revised model and new methods that may help resolve some of the above problems often bring a new set of problems of their own. They can even compound error rather than improve accuracy. In light of this, there appears to be still a long way to go before a

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comprehensive dune mobility prediction system becomes available.

Acknowledgements All GCM data were downloaded from the IPCCDDC gateway, and the Hadcm3 GCM data and the observed CRU data set were supplied by the Climate Impacts LINK Project (DEFRA Contract EPG 1/1/ 124) on behalf of the Hadley Centre and the UK Meteorological Office.

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