http://www.earthweek.com/2009/ew091106/ew091106c.html

Yerupaja mountain in the high Andes may be one area to be painted white after glaciers melt.

A Peruvian scientist told his country’s parliamentary commission on climate change that global warming could be combated by painting highly reflective white paint over rock and ground exposed by receding glaciers.

Eduardo Gold, president of Glaciers of Peru, says reflecting the sunlight would keep the ground from absorbing as much solar radiation.

He proposes using only environmentally friendly lime-based paint made without harmful chemicals.

His scheme has drawn interest from the World Bank, which is considering various “geoengineering” proposals to counter climate change.

Gold says hiring local residents to do the landscape painting could create about 15,000 jobs.

The glaciers of the tropical Andes have proven to be very sensitive to climate change.

Some scientists have estimated a loss in coverage of more than 30 percent over the past 30 years.

Remember on the 9th or 10th of November I said the Alps were no longer grwoing due to plate tectonics. When I first read the article below, I thought I had made a mistake, but on closer inspection, I found I wasn’t!

From http://www.sciencedaily.com/releases/2009/11/091105121207.htm

ScienceDaily (Nov. 6, 2009) — The Alps are growing just as quickly in height as they are shrinking. This paradoxical result comes from a new study by a group of German and Swiss geoscientists.

The formation of the Alps through the collision of the two continents Africa and Europe began about approximately 55 million years ago. This led to the upthrusting of the highest European mountains, which probably already achieved its greatest height some millions of years ago. At present, however, the Swiss Alps are no longer growing as a result of this tectonic process.

But Swiss geodesists, who have already been measuring the Alps with highest accuracy for decades, have observed, however, that the Alp summits, as compared to low land, rise up to one millimetre per year. Over millions of years a considerable height would have to result. But why then are the Alps not as high as the Himalayas? This is because the  mountains eroded at almost exactly the same speed as they were being built.

How does it come about now that the Alps erode at the same speed that they rise? “Here pure upthrusting forces are at work. It is similar to an iceberg in the sea. If the top melts, the iceberg surfaces out of the water  increases by almost the same share,” explains von Blanckenburg.

Thus this paradoxical situation with the Alps that through wind, water, glaciers and rock fall, they are being constantly finely eroded from the top but on the other hand, regenerated from the Earth’s mantle. This phenomenon, even if already postulated theoretically has now been proven for a complete mountain range for the first time.

Thus, the Alps are constantly rising, although they have been deemed “dead” in a tectonic sense ( because they plate movements have ceased a long time ago). Instead of plate forces it is the strong climatic variations since the beginning of the so-called quaternary glacial before approximately 2.5 million years, to which mountain slopes in particular have been reacting so sensitively. This holds the Alps in motion.

Think ships: if you laden a ship with a heavy load, it sinks down low in the water. If you take out some of the cargo then the ship will rise up higher in the water. But if the stuff you remove from the ship is only from the top deck, the height of ship + cargo will get less, but the height of ship above the water will be about the same as it was before.

What in effect they are saying is imagine the Alps as a piece of crust ‘floating ‘ on the mantle. As the mountain tops are eroded, the overall mass decreases and so the Alps ‘float’ higher than before.

More than a thousand Sumatrans were dead in the wake of two strong earthquakes in as many days. Officials believe the death toll will rise further.

The initial quake registered a magnitude of 7.6 late Wednesday afternoon as it wrecked homes and other buildings, including hospitals, in the city of Padang.

The shaking also sparked fires and broke water mains, causing streets to flood across the city.

Thousands of victims were feared still buried beneath the rubble two days after the first quake struck.

An earthquake has struck southern Iran, with hundreds of people suffering injuries, a state news agency says.

There are no reported deaths but about 100 people needed hospital treatment, the Irna news agency said. The 4.9-magnitude quake in Bandar Abbas sent people streaming into open spaces and cut power cables in the city.

Iran straddles a major geological fault line, making it prone to tremors. In 2008, seven people were killed in an earthquake in the same region. On average one earthquake hits the country each day, although most are minor tremors and are often in sparsely populated regions.

http://earthobservatory.nasa.gov/NaturalHazards/view.php?id=40954&src=nha

At 06:00 Universal Coordinated Time on October 30, 2009—an hour after this image was captured—the U.S. Navy Joint Typhoon Warning Center reported that Typhoon Mirinae had sustained winds of 85 knots (155 km per hour) with gusts up to 105 knots (121 mph), making it the equivalent of a Category 2 hurricane on the Saffir-Simspon scale. It was heading west toward the Philippines at about 13 knots (15 mph); the storm was churning up 8.2-meter waves. The eye of the storm was a large mass of roiling clouds located less than a hundred kilometers northeast of Cataduanes Island in the Philippines. Mirinae is the fourth typhoon to hit the Philippines this season, and heavy rains are likely to bring additional flooding.

For more about it: http://news.bbc.co.uk/1/hi/world/americas/8297276.stm

Conclusion

Hypothesis 1: The temperature will remain the same at all sites

The data showed very similar values (although slightly cooler near the top of the course). This may have been because site 1 was the most open of the 4 measured

Hypothesis 2: The dissolved oxygen will increase with the presence of white water

This was true. However the lack of sensitivity in this test does not make it particularly useful in testing for example for organic decay causing a reduction in oxygen. A more sensitive test might have shown differences between sites 1, 3 and 4.

Hypothesis 3: The pH will be more acid where there is organic pollutants

The only real evidence of organic pollutants was the sheep hoof marks at the ford and this did have the most acidic results. The anomaly here though was the alkaline (pH8) at the waterfall. Having sort the advice of a chemistry expert, we decided that there are 2 possibilities for this.

(a) Experimental error

(b) High levels of dissolved CO₂ can cause a lowering of pH as some of the CO₂ becomes dissolved in the water to form a weak acid, carbonic acid. So lack of CO₂ may mean that the water has a higher pH than expected. At the waterfall, the water was shallow and the rock was smooth and there was no evidence of either plant or animal life, so it is possible that low CO₂ could account for the high pH.

Hypothesis 4: The turbidity will be the same at all sites

True

Hypothesis 5: The velocity will decrease the further the site is from the source

This hypothesis was obviously false. There are 3 possible causes of this:

(a) Experimental error. See below for more detail on this in the evaluation.

(b) The theory is that the slope of the river bed is steeper the nearer the source you are. It is the slope that determines the velocity of the river. So on average, the course of the river tend to be steeper the nearer you are to the source.

However, there are a number of factors to be born in mind when applying this idea. The Nant Gwynllyn is in no way uniform as the diagram implies

(c) The other factor that needs to be taken into account is the bedload. Large angular rocks and hard resistance channel sides and base force the water to go around the obstructions. These create friction with the water, both of which slow the water down. This was demonstrated clearly by the path of the orange that was rejected in site 4!

Hypothesis 6: The estimated cross-section will increase the further the site is from the source

Hypothesis 7: The estimated discharge will increase the further the site is from the source.

Both these hypothesises were shown to be just about true from the data. Although (see in the evaluation) how valid the figures were leaves is open to question. The data also showed that the width and depth can vary quite a lot, both reducing and increasing over the distance examined, but it their combination that shows some consistent increase, the further downstream the site is.

Evaluation

Water quality:

The kit was complete and simple to use. The instructions were clear so as to make the best possible use of the test available. However, the lack of sensitivity of the dissolved oxygen test means that tests over one water course is unlikely to show any great variation as there is only one ‘moderate’ value (4ppm).

What else I could have done: I could have tested more sites, although given the similarity of the river chosen, I am not sure if this would tell me anything. However, what I can and will do, is to retest those same sites when the environmental conditions change. For example, we had had little rain in the preceding 4 weeks and the weather was unusually mild for the time of year. So I will retest after heavy rain when it is colder and also in high summer, when the water is even lower.

River Feature

These tests could have been carried out with more care as I do not feel that I got accurate enough results to be sure of the conclusions.

3 oranges placed is a fairly random fashion did not give clear enough results. Next time (on the retest – see above) I will carefully measure every 20 cm and drop another one in. (This would only be possible on a small stream like Nant Gwynllyn. On larger rivers you could do 50cm gaps for example)

The measurement of maximum depth in one place only and its use to estimate the cross-section was not good enough.

All in all I enjoyed the experience and have learnt lots about how to do it better next time.

Definitely a bit of a struggle to find different ways to graph the data. Not really happy with the result, but I cannot do any better!

This graph shows that

1. The lowest pH was at the ford  (site 3) which was adjacent to the field with sheep in. There was evidence that the sheep used the stream to drink from. There was no other source of water in that field and there were hoof prints on one side of the ford. The cause of higher pH at the waterfall (site 2) is not known and a bit of a mystery.
2. The temperature of each site was similar, just a little lower than the air temperature in each case. This is as would be expected, as the mild day time tmeprature increase the air temperature faster than the water temperature.
3. The dissolved oxygen levels are very approximate by this test, but certainly gave the expected results, with the water that has just fallen of the falls having the highest oxygen content.
4. No turbidity was recorded so it was not put on the graph. However

For the velocity graph, the values were re-arranged in each case, into minimum and median and  maximum values.

By doing this it was easier to show that not all the surface water, which the floating orange shows, is the same at any one site. In fact in one case on on site 4, the orange took 299.97 sec to cover 10 metres, as it got into a back loop and returned up stream twice before it managed to pass the 10 metres mark. This sample was discounted, at it would have had an unduly large impact on the average velocity and hence the discharge. However, had say 10 measurements of that river been included, then including that one would havegiven a fairer outcome.

The X-section graph demonstrated that regardless of width or depth, that the x-section does increase down stream, and finally looking at the discharge, these results mirrored that of the x-section very closely

Tomorrow the evaluation and conclusion – it will be fairly damning as I could have done a lot better!

Here are the the tables of data I collected

This is the water quality data

This is the velocity chart: reember the velocity calculation  = distance (10 metres)/time

This is the width, depth, estimated cross-sections, and discharge data – the cross-section estimate I used was

1/2 x width x depth; the discharge = x section x vel

My next job is to find a good way to present these graphically – it will not look as neat on a blog  as it would in a word doc. but I will see what I can do!

Methods

Water quality:

Temperature:

What was needed: Thermometers strips,  0.5 m of down pipe as explained below, watch with second hand.

The 2 thermometers (one low and one high temperature) were both on sticky backed strips. These were stuck to the bottom edge of a piece of down pipe. The thermometers needed to be kept 10 cm below the surface of the water for 1 minute. So a tape was fixed 10cm from the end of the pipe with the thermometers on, that gave the level and keep the hands dry (and warm). The down pipe was lowered into the water until it reached the level of the tape and timed for I minute. The temperature was recorded.  The thermometer strips showed temperatures rising in gaps of 2. If 3 strips were highlighted, the middle one was recorded. If 2 adjacent number bars were highlighted equally, the average of the pair was recorded.

Dissolved oxygen

What was needed: chemical test tablet, the small tube from the kit, a watch with a second hand, the conversion chart for ppm (parts per million) against temperature.

The small bottle was filled up to the top with water. 2 tablets were added. The bottle was left for 5 minutes. The colour was tested against the colour chart and recorded. Using the conversion chart, the ppm was also recorded.

pH

What was needed: chemical test tablets,  a tube with lid provided in the kit, colour chart.

A sample of water enough to fill tube to the 10ml marker was collected from the river. 1 testing tablet was added and the top was placed on the bottle. It was shaken until the tablet had dissolved and then the colour was checked against the colour chart and the pH was recorded.

Turbidity

What was needed: turbidity disc, container from the kit, comparison chart.

The disc was fixed to the bottom of the container. The water was taken from the river to fill the container and the visibility of the disc was assessed by comparison with the chart. The result was recorded.

The velocity of the river:

What was needed: a supply of aging oranges free from the veg shop, a 30m tape, a stop watch, a fishing net to catch the oranges.

A 10 metre distance was measured along the river bank. The start and the finish was marked. 3 oranges were dropped in the river in different spots across it. This was because it was obvious that the water passing through in different parts of the river varied in velocity and so to get a full range gave a better estimate of the average surface velocity.

The time taken to go from start to finish was recorded. The oranges were hopefully caught after they had passed the finish. Using velocity = distance/ time, the velocity was estimated and recorded. Then the average surface velocity could be found by adding the 3 results and dividing by 3.

The width, depth and cross-section of a river, discharge:

What was needed: the down pipe (to measure depth), the tape (to measure width), velocity measurements

The width was measure using the tape. By exploration, the maximum depth, along the line where the width was measured was also recorded. These 2, given the narrow width of the river, was seen as sufficient to estimate the cross section

Triangle A = ½ small base x depth

Triangle B = ½ long base x height

Total cross-section area estimate = ½ small base x depth + ½ long base x height

= ½ height ( short base + long base) = ½ height x width

Discharge = velocity (average) x total cross-section estimate