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As beautiful as Kenwood's CH250 food processor will look on your kitchen counters with its brushed die-cast steel finish, it's not all about appearances. Inside you'll find a set of four stainless steel blades instead of just two, powered by a 500 watt motor that should in theory chop and crush in half the time.
The blades are also stacked in a two-tiered design to more thoroughly puree your food, but if a coarse chop is all you're after the top set can be easily removed. The $50 variable speed power button is also located on top of the processor so while being pressed you're putting extra pressure on the lid, preventing unwanted eruptions.
[Kenwood via Appliancist]


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Search engine activity remained flat in June, with Google hanging onto its immense 65.5 percent market share while other search engines also held their steady audiences.
Second to Google was Yahoo with a static 15.9 percent, then came Microsoft with a slight rise from 14.1 to 14.4, Ask.com with a steady 2.9 and AOL with a slight drop from 1.5 to 1.4, according to comScore, a digital data research firm.
The relatively flat report signifies to us that users are reluctant to change search engines. A Google user is very unlikely to start using AOL for search, and vice-versa. Each search engine is serving its population satisfactorily -- usually a good thing. But Google's 10.9 billion searches and market share dominance creates fear and loathing in its competitors -- and part of the reason it's facing a government probe, with much of it spearheaded by the No.3 of search, Microsoft.

subset: commoncrawl


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I posted a blog on this subject several days ago and said that my own personal challenge would be that: "… I have set myself the goal to get things well and truly properly sorted into proper categories in all my files and folders, so if I want to look for a family treasure, like my uncle's letter, I can put my hands straight on it. If I want to find a particular parish register transcript I will find it straight away instead of searching through pile after pile of mixed paperwork. The same with Wills and Certificates and so on. And if possible to do more than that, maybe I will also separate these things into maternal and paternal sides of the family piles"!! I added a photo that showed just a fraction of my piles of paperwork!!
Now my Challange is completed for this week and I have managed to sort a lot of things out and found things that have been tucked away in piles since 2001. Here is the new photo as things are now – the bookcase looks just as full but the difference here is that every single plastic folder has been sorted into different categories with a note informing me what category is in there and what I have to do with it, such as add the info to my family tree programme, or write to certain places for further info and the binders and albums each have their own type of thing in, such as one A3 binder only has photos and photo albums in it, and another only has Wills in it and another only has Birth, Marriage or Death Certificates in it instead of all these things being in different places, on their own. Now they are all together in their very own category!!
Here is the new organised look:
I even went through everything in all the piles of paperwork that you could not see in my original photo and that has all been sorted into the plastic folders you can now see in their separate categories!!
And just a quick reminder of how it looked at the beginning of this challenge:
I am pleased I stuck with this because at least now when I am hunting for something, I can go straight to the relevant plastic folder or binder and know that's where it will be!
I have now also issued myself with an ongoing challenge, which is to take one of the plastic folders, either daily or weekly (depending on how much is in it) and sort it more particularly, depending on what the category is. I have Parish Register transcripts but need to sort them better into Counties as at the moment they are all mixed up, but they are in place name alphabetical order, so that is not so difficult to find a particular one!
I have enjoyed the Challenge, and this was something that was very much needed to have been done a long time ago. Hopefully everyone else has had success with their own Challenges. I went to some of your blogs and thought your Challenges were great and much more interesting than what I have tried to do!!
Onto the next Challenge now! I received my invitation from Thomas but haven't dared to look to see what it is yet – well, I was too busy with this Challenge!! 😉


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Yushan with Inset Piping
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Tomatoes are unquestionably the most popular vegetable for backyard gardeners.
Tomatoes are unquestionably the most popular vegetable for backyard gardeners. But it wasn�t always like that. In fact, the tomato is actually something of a new comer at the American table.
Believe it or not, tomatoes used to be thought of as poisonous. Tomatoes do belong to the poisonous nightshade family, which didn�t help matters, but so do the potato, pepper, and eggplant, and they have been considered edible vegetables for centuries. Part of the reason tomatoes were avoided may have been because they weren�t native to Europe.
They originated in South and Central America, and were first brought to Europe by the Spanish Conquistadors in the sixtieth century.
The English weren�t impressed by their food value; they grew tomatoes on trellises, as ornamentals. The French considered the tomatoes �peasant food� some of these attitudes probably carried over into the United States, for the tomatoes wasn�t eaten here until about the middle of the nineteenth century. Since then, moreover, the tomato has enjoyed a rapid climb to the top, and is now one of the most popular vegetable grown.
The large, red, succulent fruits that we enjoy today, however, have little in common with their ancestors, the �tomatls�, grown by the Incas and the Peruvians.
The early tomatoes were small and yellow; today�s sweet juicy tomatoes were developed by the Italians. They found their way into America and became a staple in the American diet. Since you probably have tomatoes in your backyard garden this spring, we�ll give you a few tips about the care they need to insure their best growth.
My information was provided by Extension Emeritus Specialist Jim Stephens of the University of Florida, Institute of Food and Agricultural Sciences.
Plenty of sunshine is a must for healthy sweet tomatoes. A minimum of six hours a day is suggested, but sunshine all day is even better.
Another rather general rule is to use mulch. As you know, mulches help conserve soil moisture, inhibit the growth of weeds and keep fertilizer from leaching.
But, most important for tomatoes, mulches keep the fruits off the ground. Good materials for mulches are straw, leaves, dried lawn clippings and plastic.
Mulching with black plastic has given many gardeners excellent results. If you would like to try this method, prepare the soil for planting and make sure that it�s sufficiently moist. Lay the plastic over the entire bed and anchor the edges with soil. Then, cut holes in the plastic and set the plants into the bed.
Tomatoes need about 1 � inches of water each week heavy soaking once or twice weekly are better than many light sprinklings. If your soil is very sandy, you may need to water more frequently, especially the first week after planting. Never let the soil completely dry out.
Many insects can bother tomatoes, causing leaf and fruit damage. A general purpose insecticide will control most of their problems. You county extension agent or garden center operator can recommend an appropriate insecticide.
The most serious disease of tomatoes is blights, leaf spot, wilts and viruses. Leaf spots can usually be controlled with fungicide sprays. Viruses and wilts, however, must be controlled by using resistant plant varieties to start with.
For more information on tomato production contact the Gulf County Extension Service @ 639-3200 or visit our website: http://gulf.ifas.ufl.edu.


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There is a lot of talk today about Advanced Persistent Threats (although that phrase is falling out of favor), blended threats, zero-day exploits and other sophisticated attacks. So it is almost refreshing to see that old school Social Engineering 101 spamming appears to still be alive and well.Take, for instance, the recent e-mail I got from Coke 2012 Award with the subject line PLS' OPEN THE ATTACHMENT. Nothing in the body of the e-mail. Just a .doc attachment, presumably about an award I had won. I'm not sure if the award is from the Coca Cola Co., or if it is an award of coke (either drugs or bituminous fuel). I haven't gotten around to opening the attachment yet. It's not very high on my list of priorities.I also recently received several e-mails from Kathy in Hong Kong, offering me "the most professional industrial telephones manufacture in Asia." If I ever need a really professional Asian industrial phone, I'll drop her a line.There are many other examples of low-tech, low imagination spamming, but I can't say much about them because I can't read them. Few days go by that I don't get a half-dozen e-mails in Cyrillic, Chinese, or Arabic script. These e-mails taunt and frustrate me because I'm sure I am missing out on some great offers, but I have no idea what they are. Maybe if I open the attachments and click on all of the links, I'll get a clue.
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William Jackson is a Maryland-based freelance writer.
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Water SA
versión On-line ISSN 1816-7950
versión impresa ISSN 0378-4738
Water SA vol.35 no.3 Pretoria abr. 2009
Hydrology, sediment transport dynamics and geomorphology of a variable flow river: the Mfolozi River, South Africa
SE GrenfellI, *; WN ElleryII
ISchool of Environmental Science, Memorial Tower Building, Howard College Campus, University of KwaZulu-Natal, Durban 4001, South Africa
IIDepartment of Environmental Science, Rhodes University, PO Box 94, Grahamstown 6140, South Africa
The co-efficient of variation for inter-annual streamflow of the Mfolozi River is extremely high at 79%. An analysis of flow frequency indicated that streamflow is skewed towards low-flow values, with a number of extremely large flood events occurring as outliers on the histogram. Streamflow variability in the Mfolozi River may be linked to multiple factors including a large catchment size, a seasonal climate of a dry winter and wet summer, evergreen vegetation in the catchment, variable precipitation and the occurrence of regionally pervasive climatic oscillations. This research aimed to address how streamflow variability impacted upon sediment transport and thus, geomorphology. It was found that sediment transport variability occurred at the intra- and inter-annual scale. Analysis of mean monthly sediment concentration and discharge showed a hysteresis effect, such that sediment concentration peaked prior to discharge in the early wet season. During the late wet season, peak discharges often had unexpectedly low sediment concentrations. Furthermore, data suggested the existence of long-term hysteresis that may be related to decadal-scale climatic oscillations that alter sediment availability and stream capacity, resulting in discharge peaking in 2000 and sediment concentration in 2005. However, more data are required to confirm this relationship. Variability in streamflow appears to share a causal relationship with sediment transport variability, as both are linked to variation in precipitation and the resultant impacts on vegetation growth and evapotranspiration rates. The variability of streamflow and sediment transport has implications for stream and floodplain geomorphology, and the hydrology of variable rivers should be considered when interpreting their geomorphology.
Keywords: flow variability, sediment transport, hysteresis, turbidity
Introduction and regional setting
Streamflow variability
The impact of variable river flows on fluvial geomorphological processes has only received adequate attention in dry-land environments (Tooth, 2000). However, many variable rivers do not fit into the category of dry-land rivers as defined by geomorphologists, even though they may experience similar hydrology in terms of variability. Rivers on the eastern seaboard of southern Africa, exhibiting similar variability to those of Australia, do not experience transmission losses and are not located in areas of low precipitation (e.g. Costelloe et al., 2003). Furthermore, flow is generally perennial. Despite modest precipitation on the eastern seaboard (±1 000 mm/a average), most of the region experiences a negative annual water budget due to the impact of evapotranspiration (Schulze, 1997). Tooth (2000) suggests that dry-land rivers are characterised by few large floods, with intervening low flows for the majority of the water year. This is similar to other authors' descriptions of variable rivers of Southern Africa, described as 'flashy' (Poff et al., 2006). The impact of variable flow hydrology on channel geomorphology has since been cause for much debate. In Tooth's (2000) description of dry-land rivers, large-magnitude floods are the major landscape driver, as low flows lack capacity to do extensive geomorphic work in intervening periods. Contrastingly, Heritage et al. (2001) found that the channel form of the Sabie River, South Africa, is not related to a single channel-forming discharge, but rather to low frequency, large flood events, as well as high frequency, comparatively low-flow events. In some cases, a combination of hydroclimatic trends and the occurrence of channel stabilising riparian vegetation may be important in determining channel shape (Dollar and Rowntree, 1995; Rowntree and Dollar, 1996; 1999).
Using measures of flow variability, this paper investigates how streamflow variability impacts upon sediment transport. Using the Mfolozi River as a case study, a river on the eastern seaboard of Southern Africa, it considers how flow variability may potentially affect floodplain geomorphology and processes.
Dettinger and Diaz (2000) showed that variation in annual precipitation was generally low worldwide, although variability in runoff has repeatedly been shown to be far greater in Southern Africa and Australia than in other continental areas, even when compared to areas of a similar climate (e.g. Puckridge et al., 1998; Dettinger and Diaz, 2000; Peel et al., 2001). Coefficients of variation (CV) for Southern Africa and Australia vary from 75 to 110%, as compared to global norms of between 20 and 45%. Poff et al. (2006), using principle component analysis, found that South African streams were characterised by flashy flows on an inter- and intra-annual scale, noting that streams in South Africa and Australia could be described as globally the most 'extreme'. As a result, Jenkins et al. (2005) refers to these areas as being characterised by 'boom and bust' hydrology and ecology. Variation does appear to follow some degree of cyclicity, such as the link between lower runoffs in the most variable regions during La Niña years and subsequent water years (McMahon and Finlayson, 2003; Puckridge et al., 2003; Dettinger and Diaz, 2000).
CVs of precipitation have been found to correlate only modestly with CVs of runoff (Dettinger and Diaz, 2000). Accordingly, Peel et al. (2001) found that only a small proportion of runoff variability could be accounted for by variability in precipitation. The increase in variability from precipitation to runoff indicates that variability is enhanced during the process of converting precipitation to runoff. The major contributor to flow variability has been attributed to the effects of evapotranspiration (e.g. Poff et al., 2006; Peel et al., 2001). Peel et al. (2001) showed that evapotranspiration was greatly increased in areas that were dominated by evergreen, as opposed to deciduous, trees. In addition, he found that climates with wet summers and dry winters were more conducive to increased runoff variability. Similarly, Dettinger and Diaz (2000) reported that summer precipitation generally contributed less to streamflow than did winter precipitation. A combination of these factors is likely to be the cause of heightened streamflow variability in Australia and Southern Africa. A correlation between catchment size and streamflow variability has also been noted by some authors (Peel et al., 2001; Dettinger and Diaz 2000; Puckridge et al., 1998). Overall, streams with large catchments, that receive most of their rainfall in summer, and that have greater proportions of evergreen trees as opposed to deciduous trees can be expected to experience the greatest amount of variability in streamflow.
The Mfolozi River, KwaZulu-Natal
The catchment of the Mfolozi River drains a 11 068 km2 portion of northern KwaZulu-Natal on the eastern seaboard of Southern Africa. The KwaZulu-Natal region was rejuvenated 20 million years ago (Ma) and 5 Ma when Southern Africa experienced 2 major uplift events that lifted the eastern seaboard by 250 m and 900 m respectively (Partridge and Maud, 1987). As a result, the region is currently in a long-term state of incision, with rivers considered to be relatively steep and fast flowing.
The Mfolozi River comprises two major tributaries, the Black Mfolozi, which arises in the north approximately 1 500 m a.m.s.l., and the more southerly White Mfolozi, which arises at an altitude of 1 620 m. The two rivers converge approximately 50 km west of the Mfolozi River's mouth to the sea. The majority of the catchment is under natural vegetation cover, largely because much of the catchment falls with the Mfolozi-Hhluhluwe Nature Reserve. Sixty percent of the remaining natural vegetation is grassland, with lesser areas of thicket and bush (21%) and natural forest and woodland (15%). Besides natural vegetation, just less than a quarter of the Mfolozi catchment falls under agriculture, the majority of which is small-scale subsistence and commercial forestry, and 13% of the catchment has been classified as degraded through overgrazing or excessive resource use. Less than 1% of the catchment is urban, with the major industrial centres of Mtubatuba, a timber and sugar-cane processing town, in the lower catchment and Vryheid, a coal-mining and quarrying district, in the upper catchment. The floodplain itself is predominantly used for the cultivation of sugar cane (65%), while the remainder falls within the iSimangeliso Park (previously St. Lucia Wetlands Park).
Precipitation is largely restricted to the summer months when approximately 80% of the rainfall occurs, peaking between November and April (Tyson, 1986). Mean annual precipitation in the catchment varies from 1 288 mm at the coastal town of St. Lucia, to 667 mm/a in the Umfolozi Game Reserve in the mid-upper catchment, to 914 mm/a at Nongoma in the upper catchment. Mean annual potential evapotranspiration is generally more than double that of precipitation, with atmospheric demands averaging 1 800 mm/a (Schulze, 1997).
Heavy rainfall is generally associated with easterly low-pressure cells that can remain in the region for up to 10 d (Tyson and Preston-Whyte, 2000). The occasional occurrence of tropical cyclones may lead to extremely high rainfall in the catchment, and therefore long recurrence interval flood events on the Mfolozi River. The most recent such event was that of Cyclone Domoina in 1987, which resulted in a peak discharge of approximately 16 000 m3·s-1, which constitutes approximately 3 times the 100-year return period flood. During the flood, current velocities of 2.6 m·s-1 were measured (Travers, 2006). The main mechanism for winter rainfall is the passage of cold fronts and coastal low-pressure systems. The Mfolozi catchment is thus at the interface of weather-producing systems derived from the south, in the form of mid-latitude cyclones, and weather systems originating towards the north in the tropical easterlies, such as easterly waves and lows, and occasionally tropical cyclones.
Catchment precipitation and streamflow of the Mfolozi River
To assess seasonal aspects of rainfall in the catchment, precipitation records from 5 weather stations in the catchment were obtained from the South African Weather Bureau. Stations were located in the upper Black and White Mfolozi River catchments (Hlobane and Goedgeloof, n=89 and n=64 years respectively), in the mid-Black and White Mfolozi River catchments (Mbhuzana and Mahlabatini, n=24 and n=89 years respectively) and below the confluence on the coastal plain (Uloa Agricultural Office, n=75 years) (Fig. 1).
Three streamflow series were obtained from the South African Department of Water Affairs and Forestry for the Mfolozi River. Two of the data sets were of the major tributaries of the Mfolozi River, the Black and the White Mfolozi Rivers. The 3rd flow series was of the Mfolozi River below the confluence of its 2 major tributaries on the lower coastal floodplain (Fig. 1). The tributary flow records were much longer than those available for the Mfolozi gauge located on the lower floodplain, with only 10 years being available for analysis as compared to 40 years at the upper gauges.
The median annual discharge was calculated using all available records from each of the data sets. In addition, a mean annual discharge for each year was calculated in order to allow an inter-annual comparison. A percentage deviation from the long-term median was then calculated for each year in each data set in order to establish long-term variation. The amount of correlation between the gauging stations in terms of percentage deviation was also determined. A frequency analysis of percentage deviation values allowed an investigation into the comparative number and severity of wet and dry discharge years. Since the period of record for the Mfolozi River was short, the analysis was run using data for the Black and White Mfolozi Rivers.
In order to test relationships between rainfall and discharge in different areas of the catchment, correlations between rainfall (percentage deviation from the median of total annual precipitation) and discharge (percentage deviation from the median of annual mean precipitation) were also calculated. The co-efficient of variation (CV = standard deviation/mean x 100) for each of the discharge and rainfall gauge records were computed. In addition, the water budget for the quaternary catchment in which each rainfall gauge was located was calculated from Schulze's (1997) values of mean annual precipitation and potential evaporation.
Sediment transport
Sediment flux was assessed at 5 straight reaches on the lower Mfolozi floodplain between the 6th and 11th of March 2006 (A to E on the inset Fig. 1). Sites were selected such that they were equally spaced and represented areas of different floodplain slope. The same sampling method was used at Site A to E and took approximately 8 h.
Bed-load sediment
Bed load was measured using a Helley-Smith bed-load sampler constructed to the specifications of Emmett (1980) with a weight of approximately 30 kg and a sampling bag with a 0.25 mm mesh size. The weight of the Helley-Smith ensured there was no frictional drag as it was lowered into the water. At Sites A to E, 3 traverses were completed 10m apart to overcome the effect of channel bed-forms (Emmett, 1980; Carey, 1985). Each traverse was divided into 5 subsections and bed-load sampling was conducted at each one. Initially, 4 samples per subsection were collected, but this was subsequently reduced to 3 to ensure that the entire data set was collected within the day and error due to changing discharge could be reduced (e.g. Kleinhans and Ten Brinke, 2001). Unfortunately, this is likely to increase errors associated with flow and transport variability (e.g. Pitlick, 1988; Gaweesh and Van Rijn, 1994; Kleinhans and Ten Brinke, 2001), but was considered to be less important than error incurred through changing discharge. Overall, between 45 and 50 samples were collected at each sample site, exceeding Gomez and Troutman's (1997) recommendation of 40 samples to reduce random and systematic error. Sampling time per sample was between 3 and 4 min, and was measured to the nearest second.
Bed-load samples were dried and weighed. The dry weight of the combined samples at each subsection was used to calculate sediment flux and sediment discharge respectively. While the sampling period was limited to one week, the purpose of sampling bed-load sediment transport was not to calculate a long-term bed-load sediment transport value, but to determine the relative amount of bed-load sediment transported down the length of the river at the different sampling sites. The limited sampling period was considered to be suitable for this requirement. Dried samples were sieved in order to establish particle-size distribution.
Suspended sediment
Suspended load was sampled on the first of the three traverses at each sample site. Samples were taken at each of the 5 subsections at variable depths using an Eijelkamp Watertrap sampler, with a cylindrical volume of 1.22 ℓ. The aim was to sample at 0.5 m depth intervals, but it was sometimes necessary to sample more frequently when the channel was shallower than 1 m. The samples were transported in 2 ℓ plastic bottles for laboratory analysis. Particle-size of 10 of the suspended sediment samples was measured using a Malvern Mastersizer.
Turbidity was measured for each of the 38 samples using a calibrated turbidity meter. The sediment concentration of each sample was calculated by evaporating the sample, and then weighing the remaining sediment. It was assumed that the addition of dissolved solids to the suspended sediment through evaporation was negligible. The suspended sediment concentration was used to calculate overall suspended sediment discharge for each sample site in kg·s-1. These data were of limited use in terms of determining long-term suspended sediment trends as the data set had too few data points for the development of a sediment rating curve (e.g. Horowitz, 2002; Ferguson, 1987). However, sediment rating curves, representing the average relationship between discharge and sediment concentration, are not sensitive to seasonal variations in sediment transport, antecedent conditions and differences in sediment availability (Asselman, 2000), which were the effects under investigation. Furthermore, it seemed likely that strongly seasonal rainfall in the catchment would further increase scatter around the regression line, causing inaccuracy (Ferguson, 1986; Asselman, 2000).
To overcome the problem of developing a long-term understanding of sediment transport, an indirect measure of sediment concentration, the relationship between sediment concentration and turbidity was used (e.g. Walling, 1977). A 6-year record of turbidity, with records maintained every 1 to 2 h every day, was obtained from the Mtubatuba Waterworks located at the head of the floodplain. Sediment concentration for the turbidity record was calculated using the relationship between the known sediment concentration and turbidity of the 38 samples collected in this study. Lenzi et al. (2003) and Riley (1998) noted that using turbidity as a proxy for suspended sediment load could be erroneous if there was large variation in sediment mineralogy and particle size, or if the water contained high amounts of organic matter. However, Walling (1977) suggested using turbidity as a proxy was appropriate when sediment particles were clay- and silt-sized and catchments consisted of relatively homogeneous rock types. Since the R2 value for turbidity and sediment concentration was greater than that of Riley (1998), it was assumed that the error would remain constant, and therefore would not materially affect the study. Gippel (1995) suggested that this is a reasonable assumption, since sudden temporal changes from purely organic to purely mineral loads are rare in nature. Furthermore, Gippel (1995) also argued that the close correlation between sediment concentration and turbidity suggests that particle size variation in streams is either not usually great, or that particle size variations do correspond with changes in sediment concentration. As such, turbidity records were used to calculate mean annual and monthly sediment transport rates.
Depth profile, flow velocity and channel gradient
Along each transect, depth was measured using a weight attached to a measuring tape, while velocity was measured at frequent (every 0.5 m or less) depth intervals using a SEBA-current meter with a 125 mm diameter propeller. Both depth and velocity were measured at 4 to 6 m intervals across the channel such that an accurate cross-section and velocity profile could be drawn. Mean current velocity was calculated using an area-weighted average for each channel cross-section, which was then used to calculate discharge.
Channel gradient was calculated from the sample site's position on a longitudinal profile surveyed in April 2005 using a differential GPS with an on-site base station and roving GPS receiver. Results were accurate to within approximately 1 cm in x, y and z co-ordinates following processing and correction.
Seasonal variation in streamflow
Precipitation in the catchment varies substantially in terms of timing and amount (Fig. 1). Rainfall at Hlobane Station, located in the north on the boundary of the Black and White Mfolozi catchments, and Goedgeloof Station, in the White Mfolozi River catchment, both peak in December. Mahlabatini Station contrastingly experiences a peak during January. Precipitation in the Mbhuzana and Uloa regions occurs as 2 distinct peaks; the 1st (lesser) peak occurs in November, while the 2nd (larger) peak occurs in February.
Seasonal discharge patterns of the Black and White Mfolozi Rivers are relatively similar (Fig. 2). Both rivers experience 2 peaks in discharge, one smaller peak in December, followed by a larger peak in February. The White Mfolozi River is larger in discharge than the Black Mfolozi River, and has a larger catchment (see Fig. 1). The Mfolozi River, as measured at the coastal gauge, shows only one distinct peak that occurs in January. There are substantial un-gauged tributary inputs below the two tributary gauges (see Fig. 1). As such, there is often a difference between the combined flows of the Black and White Mfolozi Rivers, and flow of the Mfolozi River. However, in 16% of the months on record, the combined discharges of the Black and White Mfolozi Rivers were found to exceed that measured on the Mfolozi River at the W2H032 gauge, suggesting that in these months discharge decreased downstream.
The relationships between rainfall stations and flow gauges were found to be positively correlated, generally with significance between p=0.01 and p=0.05. The positive correlation between rainfall variation at Mahlabatini and Goedgeloof rainfall stations, and the Mbhuzana and Hlobane Stations was significant at the 99% confidence level. Similarly, the correlation between discharge measured at the Black Mfolozi gauge and rainfall at Mahlabatini was also highly significant (p < 0.01). Discharge on the Mfolozi River and rainfall at the upper Goedgeloof gauge was significant at the 95% confidence level.
Contrastingly, the positive correlation between discharge on the White Mfolozi River and rainfall at Mbhuzana was not significant (p > 0.05). Discharge measured at the Mfolozi gauge was also correlated positively with precipitation at Hlobane, Mahlabatini, Mbhuzana, and Uloa, although this was not significant (p > 0.05).
Inter-annual variation in streamflow
The short duration of the Mfolozi River record made it unsuitable for long-term flow analysis. However, the strong correlation between the Black and Mfolozi Rivers, and the White and Mfolozi Rivers suggests that the long-term pattern is likely to be similar to that of its tributaries. As such, flow measured at the Black and White Mfolozi gauges is used as a proxy for variation of the combined flow. Variation from the median was plotted as a time series for the Black and White Mfolozi Rivers (Fig. 3). The moving average trend lines for each station display a similar pattern to each other. The most notable error in the data is the absence of a very high discharge in 1984 for the White Mfolozi River. However, in that year, a large deviation from the median is obvious on the Black Mfolozi River, the flood which it records damaged the gauge of the White Mfolozi and this result is therefore incorrect. Similarly, damage to both the gauges during a large flood in 1987 has resulted in the deviation from the median in 1987 being underrated.
Nevertheless, there are several succeeding periods of dry and wet years represented in the data. Flow is below the median between 1965 and 1971, above the median from 1972 to 1978, and then below the median again between 1979 and 1983. A large flood marked 1984, and 1985 was also characterised by flows greater than the median flow, whereas in 1986 flow was less than the median flow. Two more generalised periods of greater than median flow occurred from 1987 to 1991 and between 1996 and 2001. The intervening period, 1992 to 1995 was a period of relatively low flow, as was 2002 and 2003.
A frequency histogram of percentage deviation from the median of mean annual flow on the Black and White Mfolozi Rivers is shown in Fig. 4. The frequency distribution differs quite strongly from a normal distribution, with a definite negative skewness. Despite the strong skew towards values lower than median, it is the set of positive deviations that have the greatest number of outliers (i.e. at + 250% and above). There are no similar outliers with a negative deviation.
The coefficients of variation for all of the discharge gauge records were high. The Black Mfolozi had the lowest CV at 61%, followed by the White Mfolozi at 69% and the Mfolozi River at 79%.
The Mbhuzana precipitation gauge showed the greatest amount of inter-annual variation, with a CV of 36.6%. This was followed by Mahlabatini (30.8%), Uloa (29.9%), Hlobane (26.6%), and Goedgeloof (22.6%). All the precipitation gauges are located in areas of a negative water budget, whereby atmospheric demand exceeds precipitation. The water budget, calculated for the quaternary catchment of each gauge from Schulze (1997), showed that Mbhuzana had the greatest atmospheric water demand in relation to precipitation, with an annual water deficit of 1 145 mm. This was followed by Goedgeloof (-1 132 mm/a), Mahlabatini (-1 077 mm/a), Hlobane (-1 019 mm/a), and Uloa (-621 mm/a).
Channel morphology and hydrology during low flows
Data collection during a week of March 2006 was found to correspond with the falling limb of a flood wave, as indicated by stage height measurements taken from the Uloa Bridge gauge (Fig. 5). Decreasing stage height corresponds with decreasing discharge measurements taken at various parts of the floodplain over the time period of the study.
Channel width varied from 29 m at sample Site B to 41 m at Site D (Table 1). Transects B and C were found to have the fastest average flows, with a velocity of greater than 0.5 m2·s-1 at each (Fig. 6).
River gradients for each sample site were calculated from their location on an accurate floodplain longitudinal profile. These gradients therefore represent a regional stream gradient, rather than a localised stream gradient. Sample Site A had the highest stream gradient of 0.06% (Table 1). This was followed by sample Site C at 0.05% and Site B at 0.03%. Sample Sites D and E were located in an area of uniform stream gradient, with a slope of 0.02%.
Bed-load sediment transport
Bed-load discharge per unit width was found to correlate strongly with mean velocity (R2 = 0.9086), displaying a linear relationship when plotted logarithmically. Sediment transport was most closely related to velocity using a power function.
The particle size distribution of bed-load sediment during the period of study was relatively constant, with all transects having an average D50 particle size of medium grained sand, with the average D50 bed-load particle size being 0.35 mm in size. All particle size distributions were unimodal.
Comparisons of bed-load discharge between sample sites was complicated by the falling flood wave over the study period (Fig. 5). Nevertheless, bed-load sediment discharge was greatest at sample Sites B (1.02 kg·s-1) and C (1.08 kg·s-1), where velocities were greatest. Site A had the lowest bed-load sediment discharge, with 0.45 kg·s-1, while Sites D an E had bed-load discharges of 0.82 and 0.84 kg·s-1 respectively.
Suspended sediment transport
The lower Mfolozi River was dominated by suspended sediment loads, with ratios of suspended load to bed load varying from 2.7 and 10 to 1 during the sampling period. The D50 particle size of suspended sediment averaged 0.0052mm (very fine silt). Suspended sediment concentration was most closely related to discharge on a linear scale, with a regression co-efficient value of 0.6473. However, the small number of values on the regression line makes the development of a sediment rating curve invalid, as discussed previously.
Average suspended sediment concentration (kg·m-3) of the 38 samples was most closely related to turbidity (R2 = 0.9567) when a power equation was applied. The power equation, where T equals turbidity (ntu), is:
[suspended sediment] = 0.0093 T 0.674
Thus, the turbidity record from the Mtubatuba Waterworks was used to construct a record of suspended sediment concentration. The suspended sediment discharge was then calculated by multiplying the suspended sediment concentration by the discharge. When the relationship between suspended sediment concentration and turbidity was applied to the years of record, it was found that sediment concentration was not significantly correlated to discharge (R2 = 0.2087). Figure 7, indicating variation in sediment concentration and discharge between 2000 and early 2006, further highlights the wide disparity between sediment concentration and discharge. Sediment concentrations were generally highest in 2004, followed by 2003 and 2005. High sediment concentrations were not always coincident with high discharges, as the highest discharges were recorded during 2001.
Streamflow hydrology of the Mfolozi River catchment
The Mfolozi River catchment comprises 3 distinct regions characterised by differences in timing of maximum precipitation. In general, rainfall occurs later in the summer season as one moves towards the coast. In the interior, rainfall peaks in December. Towards the central catchment, peak rainfall occurs in January as represented by the Mahlabatini gauge. At the coast, and towards the mid-catchment in the north, peak rainfall occurs in February. The latter regions are also characterised by a lesser precipitation peak in November. There does not appear to be an obvious regional trend in terms of amount of precipitation experienced in the catchment, except that rainfall is highest near the coast.
The January discharge peak of the Mfolozi River is probably related to a combination of high rainfall in the mid-catchment in January and through-flow from December rains feeding into the drainage network. The reasons for the 2 peaks in discharge in the Black and White Mfolozi Rivers are not well understood, although it may be related to base flow from December and January rains in combination with February inputs. Similarly, the lack of correlation between discharge at the Mfolozi gauge, and catchment rainfall is likely an indication of the heterogeneity in rainfall over the catchment.
Discharge assessed on the monthly scale showed that the Mfolozi River was on occasion susceptible to transmission losses downstream, most frequently during summer. Of the transmission losses, 60% occurred during December and January. In 53% of the cases, transmission losses exceeded 30%, while in 26% of the cases, losses exceeded 50%. However, due to the short record of the Mfolozi River, combined with abstraction of water for irrigation on the Mfolozi River floodplain, it is difficult to characterise this as part of the river's hydrology. Nevertheless, the loss may reflect a combination of withdrawal for the cultivation of sugar cane on the floodplain and the effects of evapotranspiration.
Streamflow variability
Precipitation in the Mfolozi River catchment was found to be more variable than many other parts of the globe (e.g. in Dettinger and Diaz, 2000), with coefficients of variation for rainfall in the catchment varying from 36.6% at Mbhuzana to 22.6% at Goedgeloof. These translated to even higher CVs for streamflow, which seem to be related to catchment size, with the smallest catchment of the Black Mfolozi River experiencing the least variation in discharge (61%), and the larger Mfolozi River catchment, showing the greatest variation (79%), a relationship that has also been described by other authors (e.g. Puckridge et al., 1998). In global terms, the CV values can be considered extremely high and indicate highly variable streamflow (e.g. Dettinger and Diaz, 2000).
Peel et al. (2001) found that in addition to increasing basin size, catchments with summer rainfall and dry winters were most susceptible to variable streamflow. Peel et al. (2001) argue that continued evapotranspiration during the dry months results in continual depletion of water supplies. The Mfolozi River catchment is characterised by a range of factors that exacerbate streamflow variability, including high rainfall variability, a large proportion of evergreen vegetation in the catchment, a large catchment, as well as summer rainfall combined with a dry winter.
Dettinger and Diaz (2000) have suggested that variable rivers are characterised by relatively large inter-annual variation, with typically small base flow rates that persist throughout the year. A few, large but brief floods usually determine much of the total annual flow in a given year. This statement holds true for the Mfolozi River, with a high frequency of persistent low flows well below the median. Of particular interest is the occurrence of high positive deviations from the median, which represent sporadically occurring large flood events.
A visual analysis of percentage deviation from the median suggests a discharge cycle in the region of 6 to 7 years, with considerable variation around this figure. This is consistent with what other authors have found regarding variation associated with ENSO (e.g. Pasquini and Depetris, 2007; Zhang et al., 2007). However, more sophisticated analysis, such as that done by Amarasekera et al. (1997) for tropical rivers, is required to confirm whether ENSO could be responsible for some of the discharge variability. McMahon and Finlayson (2003) found that cyclical variability, in the form of quasi-cycles, often resulted in the characteristic persistent sequence of below median flow followed by above median flow in variable rivers.
Character of bed-load sediment transport on the lower floodplain
It was found that variation in bed-load transport could provide a means for the adjustment of stream channel morphology. Since bed-load transport is related to velocity, one might expect the variables of slope, hydraulic radius and roughness, as provided in Manning's equation, to have an indirect impact on the transport of bed-load sediment. In order to determine the influence of each variable in maintaining a particular stream channel shape for a specific discharge; width, average depth and average velocity were plotted against discharge for each of the transects (Fig. 8).
Leopold and Maddock (1953) describe the relationship between these variables and discharge as follows: W=dQª, U=eQb and D=fQc, where W = channel width, U = streamflow velocity, D = depth, Q = discharge, and a, b, c, d, e and f are empirical coefficients. The relative contribution of each variable to discharge is described by the values of a, b and c. The variable that is most capable of altering to accommodate discharge along the study reach was depth (c = 0.5168), followed narrowly by velocity (b = 0.4183) and then width (a = 0.0617). This suggests that differential aggradation and erosion along the streambed, through bed-load sediment transport, is an important mechanism for accommodating discharge. Since bed-load transport is a factor of stream velocity, velocity is the second most important factor in adjusting for discharge.
In contrast to Ellery et al. (2003), stream width is the least important variable in terms of discharge. In Ellery et al.'s (2003) study, constricting vegetation on the channel margin could alter channel width, causing concomitant changes to stream velocity and channel roughness. In this study, width is not easily altered, partly because of the low-flow regime of the Mfolozi River, which decreases the potential time available for erosion or aggradation of the stream banks. As a result, channel depth is the variable that may be altered most easily to accommodate discharge.
Over a long period of time, ongoing local erosion and deposition through differential bed-load transport will have an impact on the floodplain channel slope. The current regime of erosion and deposition along the study reach will, over the long term, lead to the river's longitudinal profile approaching grade (Fig. 9).
At Sites B and C, bed-load transport increases, indicating erosion of the channel bed. At Sites A, D and E, aggradation is occurring. However, the impact on slope at each site is varied. At Site A, ongoing deposition will cause channel steepening. While at Sites D and E, channel slope will decrease as deposition occurs. Similarly, erosion and enhanced bed-load transport at Site B will cause channel slope steepening, while the channel slope at C will lessen. Erosion at Sites B and C is a classic example of the longitudinal effects of erosion, whereby the lower region becomes flattened, while the upstream region becomes steeper. Locations of erosion and deposition are consistent with geomorphic zones described by Grenfell et al. (2009).
Sediment transport variability
The turbidity-sediment concentration relationship revealed a high degree of variability in sediment transport that was not related to discharge. Using discharge as a predictor of sediment transport would be completely invalid on the Mfolozi River due to high variability in sediment transport at the inter-annual and seasonal scales.
Sediment transport on the Mfolozi River is characterised by an annual hysteresis loop (Fig. 10). Sediment concentration usually peaked prior to peak discharges, with sediment concentrations generally highest in November and December. Thereafter, sediment concentrations generally decreased during the peak discharge months of January and February. Sediment concentrations between January and September were relatively low compared with discharge, with the lowest concentrations experienced during the lowest discharge month, August. Thus, hysteresis results in sediment concentration being greater than one would expect, in comparison to the linear trend line, during the months of October to December. In contrast, the months from January to September usually have lower than average sediment concentrations for their respective monthly discharges.
Using the annual hysteresis loop presented in Fig. 10, sediment supply is greatest in the early wet season (from October to December), and thereafter gradually decreases prior to peak discharge being reached in January. Variations in sediment transport can generally be interpreted in 2 ways. Either, the low seasonal discharge of the dry months leads to sediment accumulation in the catchment, or alternatively, decreased vegetation cover during the dry months leads to increased sediment availability. In the 1st scenario, it is exclusively the lack of transport capacity of overland runoff and the Mfolozi River in the dry months that leads to increases in sediment transport at the beginning of the wet season as capacity increases. In the 2nd scenario, it is not that the river gains capacity to transport sediment, but rather that the actual amount of sediment available for transport increases. There is a connection between the 2 scenarios in that sediment may become available during the dry months as vegetation dies back, but cannot be transported to river channels without overland flow from precipitation. Since some months show much higher sediment concentrations than other months with the same discharge, sediment transport capacity of the Mfolozi River cannot be a limiting factor in sediment transport. However, sediment transport capacity during the dry season into stream channels may be lacking. As a result, hysteresis on the Mfolozi River is likely to be caused by a combination of the 2 scenarios. During the dry season, sediment becomes available as vegetation cover decreases. However, movement of available sediment into river channels can only occur at the onset of the rainy season through overland flow. This results in the characteristically high sediment concentrations of the early season, which subsequently drops as the sediment available from the dry season is completely transported. As such, accumulation of available sediment during the dry months is considered in terms of a 'reservoir' by Picouet et al. (2001). Sediment concentrations drop as the 'reservoir' is depleted.
Mean annual suspended sediment concentration was plotted against annual discharge (Fig. 11). In 2000, 1 600 x 106 m3 of water transported 897 581 t of sediment down the course of the Mfolozi River, which approximates 561 t of sediment transported for every million m3 of water. In 2001, total discharge and sediment transport both dropped substantially. However, suspended sediment concentrations were higher and the sediment discharge to water discharge ratio increased from 561.2 in 2001, to 652.8 in 2002. Sediment transport was lowest in 2002, while discharge only reached the lowest value of all years in the following year. In 2003 and thereafter, annual sediment transport gradually increased, despite generally low but increasing discharges on the Mfolozi River. The sediment / discharge ratios in 2004 and 2005 were 1 068.4 and 1 423.1 respectively.
Thus, in addition to the existence of a mean annual hysteresis loop, there is substantial variation in sediment transport on the inter-annual scale. Indeed, it appears that there may be hysteresis effects in the long term (Fig. 11). Unfortunately, additional data would be required to confirm this relationship. Nevertheless, Fig. 11 shows that during 2000 and 2001, annual discharges were greatest, but sediment concentrations were comparatively low. Contrastingly, between 2002 and 2005, sediment concentrations were high relative to annual discharge.
Since it has already been shown that variations in sediment transport on the seasonal scale are related to sediment availability, it follows that inter-annual variation is also related to temporal changes in sediment variability, except on a longer time scale. Tucker and Slingerland (1997) suggest that increases in runoff and decreases in vegetation have a similar impact on drainage basins, with both resulting in sudden increases in sediment supply through expansion of the channel network. As such, many authors assume a relationship between precipitation and vegetative land cover (e.g. Coulthard and Macklin, 2001). In the Mfolozi River catchment, persistent increases and decreases in precipitation would result in changes in vegetation cover. Following a dry period, one would expect sediment concentrations to suddenly increase as sediment accumulated on slopes becomes available for transport by flowing rivers.
Long-term variability in sediment transport may be caused by factors similar to those that cause discharge variability. Firstly, variability in precipitation directly impacts on the amount of sediment that is likely to become available for transport in any year. And secondly, variability in catchment runoff (not streamflow) impacts upon how much sediment is likely to reach a stream channel for transport. Runoff variability is impacted by several factors such as antecedent conditions, vegetation cover, but perhaps most importantly, high evapotranspiration demands that give rise to variability in Southern Africa and Australia as is described by Peel et al. (2001). It therefore seems likely that rivers with variable discharges will be characterised by variability in sediment transport that exceeds variations in discharge.
Sediment yield
In the past, inter- and intra-annual sediment transport variability has not been acknowledged, with the result that suspended sediment loads have been overestimated. Previous estimates of suspended sediment loads were based on catchment size and runoff estimations, rather than direct measurement. Considering our current understanding of the relationship between sediment availability, runoff, discharge and resulting hysteresis, the use of catchment factors and runoff to estimate suspended load is awkward. Lindsay et al. (1996) estimated a suspended sediment transport of 1.24 x 106 t·a-1, which was based on measurements of suspended sediment on 1 d in January. Rooseboom (1975) estimated suspended sediment transport at 2.36 x 106 t·a-1. The current estimation, based on the relationship between turbidity and sediment concentration over a 6-year period is 6.8 x 105 t·a-1. This translates to an average suspended sediment yield of 61 t·km-2·a-1. Thus, Lindsay et al. (1996) and Rooseboom (1975) exceeded the current and most reliable estimate to date by 560 x103 and 1 680 x103 t·a-1 respectively.
In global and Southern African terms, sediment discharge from the Mfolozi River is extremely small, contributing an average 0.68 x 106 t annually. In comparison, the Orange and Zambezi Rivers contribute 17 and 20 x 106 t each year respectively (Milliman and Meade, 1983). However, a comparison of global sediment yields vs. discharge (from Milliman and Meade, 1983) and data for the Mfolozi River does suggest that the sediment yield is relatively high considering discharge. Overall though, it appears that the misconception that sediment transport is not variable over longer time periods, and that the Mfolozi River transports sediment in the same manner as regular rivers, has previously led to overestimations in sediment transport.
Flow and sediment transport variability: implications for fluvial geomorphology
The Mfolozi River, a variable river in terms of hydrology, may be considered variable in terms of sediment transport. As emphasised earlier, variability in sediment transport is unrelated to changes in discharge. Since the causes of sediment variability and streamflow variability are linked, it seems likely that most variable rivers are characterised by sediment transport variability. Sediment transport can be seen as an indication of a river's capacity to do geomorphic work, depending on the definition one adopts. It follows then that variable rivers may not only be characterised by variability in streamflow, but also by variability in terms of geomorphic change.
Flow on the Mfolozi River is usually low and is impounded by high levees on the coastal floodplain. While the actual proportion of the time that the Mfolozi River may have exceeded bank height is unknown due to management of the floodplain for sugar-cane cultivation, it is known that for most of each year, flow in the Mfolozi River is well below the long-term median. Streamflow is characterised by 'normal' low-flow years that are frequently below the long-term median. However, in addition to long periods of low flow, the frequency of discharge distribution also indicates a tail of high-flow outliers. It is these large flood outliers that are likely to mark periods of geomorphic change, for the same reason as in dry-land rivers. Years between large flood events lack the capacity to infill or erode flood-flow features.
The extreme flood event of 1984 on the Mfolozi River may be used to explore what is meant by flood event features. The 1984 floods followed the unusually far southward movement of tropical cyclone Domoina, resulting in discharges greater than 3 times the 100-year flood recurrence interval of the Mfolozi River (Travers, 2006). These flood discharges resulted in the river avulsing towards the south of the floodplain and depositing a lobe of bed-load-derived sediment 3 km wide and 10 km long, and averaging 5 m deep. The flood event has permanently altered the surficial geomorphology of the upper floodplain region, and without human intervention, would have resulted in the development of a new Mfolozi River course. Furthermore, since 1984, the effect of floodplain deposition on surface topography and floodplain dynamics has been minimal. It appears then, that on variable rivers, where sediment transport may also be variable, major geomorphic change occurs in spurts corresponding with 'outlier' flood events. However, the extent of the impact of large floods is not uniform, and is determined by the morphology and characteristics of a particular reach (Heritage et al., 2004; Parsons et al., 2006). Nevertheless, in some systems, geomorphic change in years intervening large flood events may be negligible.
The Mfolozi River may be described as a variable river hydrologically. In addition to this variability, the river is characterised by sediment transport variability on the intra- and inter-annual scale. More data are required to clarify how sediment transport changes in the long term and to confirm whether there is indeed long-term hysteresis on the Mfolozi River. However, as it stands, it can be readily seen that sediment transport variability is not related to changes in discharge. This variability highlights the importance of scepticism in studies that use the sediment rating-curve approach to determine sediment transport regimes on hydrologically variable rivers.
However, the main aim of this paper was to call for recognition of variable rivers as geomorphically distinct from more regular rivers, on the basis of variability in geomorphic work. It was found that suspended sediment transport was supply-limited in the Mfolozi River, and that differential sediment supply was probably related to rainfall seasonality, variability in precipitation and high rates of catchment evaporation. These same factors are responsible for variability in streamflow. More research on the geomorphology of variable rivers, with particular emphasis on process rates, is required.
Research costs were funded by the Norwegian Programme for Development, Research and Education. The National Research Foundation funded S Grenfell. The Mtubatuba Waterworks supplied turbidity records that made this study viable, and the Wiggins Waterworks provided access to their turbidimeter. The authors are particularly grateful to Warren Botes and Michael Grenfell for assistance in the field.
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Received 18 August 2008; accepted in revised form 21 January 2009.
* To whom all correspondence should be addressed.
Address at the time of submission: Geography Department, Rhodes
University, PO Box 94, Grahamstown 6140, South Africa.
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- XML Dummy file included


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Cardinal Pell's statements on Jewish cultural inferiority and Genesis-as-myth create a storm – because on these things, Pell is correct.
Cardinal George Pell, not my favorite person all all (linkefest below), raised quite a few hackles with comments on the recent Q&A show (transcript here)- and unusually I can see some merit in those statement, on the allegorical nature of Genesis, and the cultural/intellectual inferiority of Jewish nations compared to others in similar times and geographies.
Of course, I do question his agenda, and have quibbles – this is Pell after all!
Jewish Inferiority
Pell labelled Jews "intellectually inferior" – an ambiguous statement, and his apology clarifies (or alters) his statement on TV. His comment about morally inferior is pretty unambigious.
If Pell did mean culturally inferior, the culture intellectually inferior, then he is absolutely correct.
After all, if doing the "What have the Romans ever done for us" sketch from Monty Python, substituting Jews for Romans, what would he have? One item – it is the culture which inflicted Abrahamism on the world … the millenia of wars, crusades, fundamentalists and hate … including George Pell himself.
The over-praised King David et al were nothing but the East Med version of the Taliban, violent religious oafs in the hills fighting the culturally literate Phoenicians (such as the Philistines) down on the coast, Philistines who were busy inventing our alphabet (the Greeks grabbed Phoenician letters, and rotated them 90 degrees, and the rest is history, written down).
Abrahamism was an "the particular sky fairy who prefers our tribe and helps us commit genocide and war crimes". The oft-criticized "sword verses" of the Koran are as warm and gentle as fuzzy bunnies compared to the celebrated (yes!) stories in Exodus and Joshua. Wipe out all the Canaanites when Joshua invaded?" And their, kids, their wives, their cattle, their goats … that's all good, a wonderful victory, to the Abrahamists.
If being happy about a genocide (whether or not it happens) of the Canaanites, but not happy about the actions of Hitler, isn't morally inferior, total bigotry, what is?
The same attitude keeps resurfacing with Abrahamism, despite the "softening" to a universal rather than tribal god during the Babylonian exile, exposed to Zoroastrianism. Oooh, those evil Babylonians, making the exiles a little more tolerant to others!
We get the crusades, we get the bigotry of Pell, we get the violence of Islamist terrorists, we get millenia, at least since the time of Constantine, of Abrahamic "you are subhuman" to people of different color, creed, gender or sexual orientation – oh – and of course, all the Abrahamist nutters who are anti-science.
Oh well, the attitude of Jews to "He who must not be named" does have resonance with a far-less vicious supernatural being familar to all Harry Potter fans.
Compared to the Romans, Greeks, Persians, Egyptians, Babylonians, Sumerians, Phoenicians, … and so many other cultures in the region, what has Jewish culture ever down for us?
Genesis Is Myth
Pell comments on the Adam and Eve myth as a religious story rather than literal truth caused another storm, with a different group of extremists.
Anybody with at least half a brain knows that it's myth, but does Pell have any choice given his other statements over the years justifying dogmatic bigotry?
There are two conflicting creation stories in Genesis, both can't be correct, both cannot be literally true, and if Pell picks one over the other, he must give his reasons for that – a level of Biblical scholarship that would undermine the entire tome – the bigotry of Leviticus and the rantings of Paul.
But Pell knows his flock, the lambs-to-the-slaughter, and the other mob who cite biblical verses as authorization to be bigots, won't have that scholarship. No harm done to his agenda as far as he is concerned.
See Also:
- Q and A transcript of Pell's comments (ABC, 2012-04-09)
- Cultural Inferiority
GEORGE PELL: Normally you go to a busy person because you know they'll do it and so for some extraordinary reason God chose the Jews. They weren't intellectually the equal of either the Egyptians or the…
TONY JONES: Intellectually?
GEORGE PELL: Intellectually, morally…
TONY JONES: How can you know intellectually?GEORGE PELL: Because you see the fruits of their civilisation. Egypt was the great power for thousands of years before Christianity. Persia was a great power, Caldia. The poor – the little Jewish people, they were originally shepherds. They were stuck. They're still stuck between these great powers.
TONY JONES: But that's not a reflection of your intellectual capacity, is it, whether or not you're a shepherd?
GEORGE PELL: Well, no it's not but it is a recognition it is a reflection of your intellectual development, be it like many, many people are very, very clever and not highly intellectual but my point is…
- On Genesis as Myth
TONY JONES: So are you talking about a kind of Garden of Eden scenario with an actual Adam and Eve?
GEORGE PELL: Well, Adam and Eve are terms – what do they mean: life and earth. It's like every man. That's a beautiful, sophisticated, mythological account. It's not science but it's there to tell us two or three things. First of all that God created the world and the universe. Secondly, that the key to the whole of universe, the really significant thing, are humans and, thirdly, it is a very sophisticated mythology to try to explain the evil and suffering in the world.
TONY JONES: But it isn't a literal truth. You shouldn't see it in any way as being an historical or literal truth?
GEORGE PELL: It's certainly not a scientific truth and it's a religious story told for religious purposes.
- Cultural Inferiority
- "Cardinal Apologises for describing Jews as intellectually inferior" (The Age, 2012-04-13)
- "Adam and Eve? That's just mythology says Pell" (The Australian, 2012-04-10).
- Other Posts on Pell
- "Pell's QuadRant essay is sooooo wrong" (2007-09-05)
- "Pell's comments could be welcomed by rationalists" (2008-07-15)
- "Pell and NSW stem cell research"" (2007-06-06).
- "God the Interview – A Club Troppo Exclusive" (Club Troppo, 2012-04-12)


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The summer season lifts off with this mega-budget action movie about a group of superheroes who band together to stop a hostile takeover of planet Earth. With Iron Man, Captain America, Thor and Hulk sharing the screen time, director Joss Whedon is forced to spend most of his time dealing out the dramatic cards -- without creating genuine dramatic suspense. A movie that seems to hover without effort, The Avengers looks good suspended in the sky, but it doesn't actually go anywhere.
Starring: Robert Downey Jr., Mark Ruffalo, Chris Hemsworth, Scarlett Johansson
Rating: Three and a half stars out of five
With so many turbo-charged talents under its comic-book wings, it's no wonder The Avengers can hover elegantly without effort, and yet, go nowhere.
An action movie that prefers to chase its own tail of dysfunction for the duration, this long-awaited gathering of Marvel characters tries to go for the psychological side of the equation, before pouring on the pyrotechnic gasoline and throwing a match in the finale.
It's not a bad bet for director Joss Whedon (Buffy the Vampire Slayer, Firefly), one of a handful of Hollywood creators who brings sincerity and earnest emotion to popcorn formats.
Whedon has a knack for bringing human truths to moronic dramatic exercises, as he proved in his surprisingly deep vampire serial starring Sarah Michelle Gellar, as well as his last scripted piece, Cabin in the Woods.
The Avengers needed a director like Whedon, because, unlike other superhero sagas, The Avengers operates as an ensemble piece, where feelings really matter, and too much edge on one ego could have proved disastrous.
To his credit, he does a good job balancing the all-star pyramid of talent headlined by Robert Downey Jr., Scarlett Johansson and Samuel L. Jackson, ensuring each bigger-than-life character is given a chance to shine.
The unlikely superhero team is united to thwart your standard "bad guy wants to take over the world" scheme.
Norse god Loki (Tom Hiddleston) is still angry at his brother Thor (Chris Hemsworth) for usurping his royal throne in the heavens, so he forges an allegiance with his space race's worst enemies: machine-like insects with green skin and metallic bones who desperately crave a forever home on Earth.
They are not nice, nor cuddly, but Loki snuggles up to their riveted breastplates so he can indulge his petty need for vengeance. His new friends will crush humanity like an anthill, but he has to get them to Earth first, and in order to do that, he has to open up a portal with a powerful energy beam.
In the opening scenes of the movie, we watch S.H.I.E.L.D. director Nick Fury (Jackson) lead a crew of researchers to a secret facility, where they test an energy cube. We learn it's something from Thor's home planet in the seconds before it goes haywire, and before you can say "Duck!" former good guys such as Hawkeye (Jeremy Renner) and Professor Selvig (Stellan Skarsgard) are pierced with an icy-blue ray that turns them bad.
Now in the service of Loki, these newly outfitted villains are poised to do battle against their old pals.
Approaching the dimensions of a superhero civil war, we're pulled into the vortex of a dysfunctional superhero family. Iron Man (Downey Jr.) is the arrogant playboy who delivers everything with a fresh coat of sarcasm. Captain America (Chris Evans) is the all-American hero who believes in morality, and old-fashioned honour. Black Widow (Johansson) is a Cold War survivor and master spy who fell in love with Hawkeye (Renner) before he lost his good-guy mojo, and Bruce Banner (Mark Ruffalo) is a mild-mannered physicist with a hulking anger problem.
Eager to exploit these incredible talents to protect the planet from all foreign and domestic harm, team leader Nick Fury tries to form a supergroup.
Fury's urgent plea is answered by the disparate heroes, but since they're all suspicious loners who've been ostracized as oddballs, they have a hard time surrendering all doubt, and blindly following their new, tight-lipped leader.
These scenes are the ones that always prove the most interesting, because they show the superheroes are thinking for themselves, opening new loopholes in the dense weave of genre.
The Avengers won't surprise anyone who has seen a feature-length comic-book movie before. Whedon does not reinvent any big wheel, and the movie unfolds almost exactly the way one might predict -- right down to the romantic flourishes and sexual asides.
So why subject yourself to all that loud noise, silly drama and unrestrained violence? Easy: It's fun.
Moreover, the bigger and goofier it gets, the more fun it becomes. Witness Hulk, a character who never found his voice until this third attempt. Thanks to Ruffalo's understated, subversive charm, Hulk gets to be the emotional scorekeeper -- ignoring all intellect, and acting on pure rage.
His "antics" prove pure eye candy, because, when the evildoers try to psych him out with the standard James Bond head games, he just picks them up and thrashes them like rag dolls. It's not only funny to see the bad guy throttled, it's payback for all the pretentious blather we had to sit through in the opening sequence, as he professed his superiority to mere mortals.
There are more than enough moments like this to keep any viewer entertained, but when it's all said, done and exploded, The Avengers doesn't leave a lasting impression, because no one really transformed on any fundamental level.
The banter is engaging, and the acting meets every minimum requirement, but the movie never exceeds the sum of its parts. It just hangs there in the sky like a decoration, waiting for the masses to worship. To really care about characters, they have to bleed. The Avengers definitely have flaws, warts and weak links in their armour, but they do not bleed -- and sometimes, a tiny red trickle from the heart goes much further than $220 million from the bank.
What's On Tonight for


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Why does NServiceBus sends subscription messages in my integration / unit tests?
When you reference the handlers assembly from your test assembly and this assembly initializes a bus instance and the endpoint to send messages to is a publisher it can happen that you see multiple messages being send to the endpoint. while you expected less messages. Inspecting the messages indicates subscription acknowledgement messages and you wonder why that is as you don't want your test project to subscribe to anything at all! This happens because NServiceBus scans all available assemblies for messages handlers and when it finds these then it will subscribe to the events which results in the messages that you see in the (journal) queues.
Luckily this behavior can be disabled by calling .DoNotAutoSubscribe() during bus initialization as shown in the following example:
var bus = Configure.With() .DefineEndpointName("integrationtests") .DefiningCommandsAs(MessageNamingConvention.IsCommand) .DefiningEventsAs(MessageNamingConvention.IsEvent) .DefiningMessagesAs(MessageNamingConvention.IsMessage) .UnityBuilder() .XmlSerializer() .MsmqTransport() .IsTransactional(true) .PurgeOnStartup(false) .UnicastBus() .ImpersonateSender(false) .DoNotAutoSubscribe() // Needed as otherwise the client will subscribe to event messages because handler assembly is references and scanned by NServiceBus. .CreateBus() .Start();
NServiceBus v3 does not automatically create queues
Queues are not automatically created anymore since NServiceBus v3. Queues are now created during the installation phase which you usually do not have in web apps or test apps. You can still have the old behavior by triggering the installation during bus creation. Make the following change to trigger this.
// From: .Start() // To: .Start(() => Configure.Instance.ForInstallationOn<Windows>().Install());
git-tfs differences between checkin, rcheckin and checkintool (ct)
I am using git-tfs for a couple of weeks now and had some issues with it that using the checkintool results in one tfs changeset that combines all previous commits. This can often be usefull but in my case I would like to push all individual local git commits seperately to tfs. This is exactly what rcheckin does and this way your git commit history is mirrored to tfs. As a reminder:
git tfs checkin
Performs one commit containing all previous commits where either the default commit message can be used (concatenation of all git commit comments) or a new custom comment.
git tfs checkintool
Same as git tfs checkin but now you can easily change which changed files to commit via the TFS checkin dialog. It shows the concatenation of all git commit comments to use as a commit comment but can easily altered in the gui.
git tfs rcheckin
Commits each previous git commit to tfs and reusing the git comment for each commit.
MVC *always* returns status code 200 when a max age is set while it can return with 304
Take a look at the following mvc csharp code:
Response.AddFileDependency(filename) Response.Cache.SetETagFromFileDependencies(); Response.Cache.SetCacheability(HttpCacheability.Public); Response.Cache.SetMaxAge(TimeSpan.FromHours(1)); return File(filename, "video/mp4")
My intension of this was. Please cache this file for one hour (SetMaxAge) and then check if the file is still valid (SetEtagFromFileDependancy) and if it is then cache it again for one hour.
However, it turns out that MVC thinks different :-). When the item is expired MVC returns status code 200 even though the clients submits a matching If-None-Match header. In such cases the webserver is allowed to respond with 304 and update the item with new information passed in the response header.
It could be that I have forgotten something in the code above but the following is the workaround I created for this situation. It needs a custom etag to use so you cannot make use of SetETagFromFileDependencies. The method basically does a Etag comparison and responds with a 304 if they match. If they do not match the method returns null but has set the above mentioned headers.
ActionResult EtagFix(string etagResponse, HttpCacheability cachability, TimeSpan maxAge) { var cache = Response.Cache; cache.SetETag(etagResponse); cache.SetCacheability(cachability); cache.SetMaxAge(maxAge); if (!etagResponse.Equals(Request.Headers["If-None-Match"])) return null; Response.StatusCode = 304; Response.StatusDescription = "Not Modified"; return new EmptyResult(); }
This method should be pasted as a method in your controller and then it should just work. The reason that I return null is that you can do the following nifty trick:
return EtagFix("EtagValue", HttpCacheability.Public, TimeSpan.FromHours(1)) ?? File(Server.MapPath("~/Content/mylargefile.dat");
The reason that this is cool is that "File(Server.MapPath("~/Content/mylargefile.dat")" will ONLY be called with EtagFix returns null. This avoids 'expensive' action result implementations to be created and do work that they do not have to do.
TortoiseSVN Subversion error " is not a working copy"
Today I got the following Subversion message via TortoiseSVN while I wanted to commit some changes.
is not a working copy
It took me a while to figure this out but it has to do with the fact that I subst the root of the branch to my S: drive. When I went to my user folder (c:usersramonsrc*) and performed a commit it just worked as expected.
The reason for the substition is that when I compile the sources that the S: drive paths are embedded into the PDB files. When another developer then attaches the debugger to any of the applications then he only needs to have the same substitution to find all source code files. Besides that, it doesn't matter on which project I work on and no matter which drive or folder the paths stay the same. This is especially usefull as I am a console and keyboard junkie.
InternalsVisibleTo and PublicKey or PublicKeyToken
A long time since my previous blog as nowadays I often tweet my ramblings but this one does not fit a tweet 🙂
Sometimes you are working with strong named assemblies and when you are having unit tests and want to access internals then you have to use the InternalsVisibleTo assembly attribute. So to discover the public key token I ran "sn.exe –tp project.publickey" and then you get the public key (long) and the public key token (short).
Microsoft (R) .NET Framework Strong Name Utility Version 4.0.30319.1 Copyright (c) Microsoft Corporation. All rights reserved. Public key is 0024000004800000940000000602000000240000525341310004000001000100ff6b089e3c4aaa a96d775c3e431ea5b8b77fe2364be31793d0baf518ba39477e7fcfc027871d184e169d70ee2940 4dc26dc66c9ab970ad9a3d78be146716e10fdc4b4d3e821a7f402498ccb30f95eaca3922075354 ecdf7bb57b20a20579cba375de71e15f298f779aad7b421f4c0ee5f7c299b528f10574f64e5610 5ea3d7af Public key token is 67178dccc283ce39
So I used the following attribute:
[assembly: InternalsVisibleTo("My.Project.Tests, PublicKeyToken=67178dccc283ce39")]
And got this nice compiler error:
Friend assembly reference is invalid. Strong-name signed assemblies must specify a public key in their InternalsVisibleTo declarations.
Then I pasted the long variant in the InternalsVisibleTo attribute and it compiled but I knew for 100% that the short version had to work. After investigation there seem two ways to pass the required strong name public key information. You can choose if you want to pass the whole public key or the public key token.
When both assemblies are signed then you need to pass the full PublicKey.
ThreadPool default thread count not useful for testing
Today I got reminded again that it is sometimes required to adjust thread pool settings. This time to test some possible connection issues and I required to open a number of connections simultaneously and also use them in parallel.
The test system is a virtual machine which only has one core so the defaults that .net uses are based on that. I first thought that the problems were caused by nunit but pretty soon found out that the case had to do with the thread pool. When I queried the current values via ThreadPool.GetMinThreads it told me that the thread pool used just one thread as minimal. After forcing that with ThreadPool.SetMinThreads to a thread count in where I could test my scenario I still had issues.
I am now using a custom Parellel.For which I adjusted so that I can set a different ChunkSize (set to 1) and ThreadCount for testing purposes.
WCF and DateTime.Kind pitfall
At my job (Company Webcast) we have several API's for our customers to use. One of those API's allow webcasts to be created and modified and that interface has data containing dates. Our platform works with UTC date time's as we are an international operating company so it is logical for use to store those as UTC.
We use WCF and yesterday we had a very weird issue where calls failed. After investigating the issue we found out that the cause was that how the date time got supplied to our service in the message
The following values are valid in an XML message:
A WCF service accepts both values but it treats them different which I did not expect! The first value became 2010-05-26 17:00 where DateTime.Kind is set to Local and the second becomes 2010-05-26 15:00 with DateTime.Kind set to UTC. This amazed me a bit as I assumed that both would always result in either a UTC or Local DateTime.
The reason it fails is that another argument states the time-zone from where the live webcast will be held. This is used in combination with the DateTime to convert the DateTime value to a local time to inform the viewer about the conditions of the event. This code assumed that the incoming DateTime value would always be of kind UTC.
So now our front-end api's convert incoming DateTime values to a DateTime value with kind UTC.
This could also be a problem when you persist this DateTime to for example a database and your storage logic does not convert the DateTime from/to UTC or Local depending on your needs. We use NHibernate for storage and it does not by default has a way to set UTC/Local to a <property> definition. This can really become a problem when time is part of your business logic as it is in ours as we use it to schedule tasks and it is very important to know it a time is UTC or not especially when something happens on the other side of the world.
How to perform a not equals comparison with NHibernate criteria api
I often hear that NHibernate is not usable for selecting records as it is not possible to perform a not equal comparison with the criteria api. I must admin that it took me a while before I found out but it really is more logical when you know. Lets take a look at the following example:
// Select entries where name = "Ramon"
var criteria = DetachedCriteria.For<MyCoolClass>()
.Add(Restrictions.Eq("Name", "Ramon"));
Your first reaction it to find the opposite of Restrictions.Eq but then you are amazed that it does not exists. The solution is so simple that it will almost embares you:
// Select entries where name != "Ramon"
var criteria = DetachedCriteria.For<MyCoolClass>()
.Add(!Restrictions.Eq("Name", "Ramon"));
Do you see the difference? I added an exclamation mark before Restrictions.Eq to invert the restrictions operator.
So now you know and probably never forget 😉
Speeding up startup time for applications that use NHibernate
I just read a very cool NHibernate trick to let your application start faster that was mentioned by Ricardo Peres:
Configuration cfg = new Configuration().Configure(); IFormatter serializer = new BinaryFormatter(); using (Stream stream = File.OpenWrite("Configuration.serialized")) { serializer.Serialize(stream, cfg); } ... using (Stream stream = File.OpenRead("Configuration.serialized")) { cfg = serializer.Deserialize(stream) as Configuration; }