Yesterday I read this story about the demise of the GABSI program which saddened me greatly, and I wanted to write a blog about this. But now it seems that the government has asked the proponents of a new coal mine to fund the program. I think this is also worth some thoughts.

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Progress has been slow, but there has been progress. I can now do the regression, get output and make this all visible on the app. It has been a bit of a hard slog, but I have learned a whole lot of new things. I have also come across a few issues that I still need to resolve, but that will be later.

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Or what he didn’t say, I think we all would like to know what really was said. I am a bit slow reacting to the meeting between Al Gore and Clive Palmer in relation to the carbon tax. This is really under the heading of “I don’t get it”, and I will try to explain what I mean by that. What I am hoping is that Al and Clive discussed business opportunities, because every time I think about climate change or even Emission Trading Scheme, I think business opportunities.

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In my last blog post , I launched this crazy plan to build a website where people can analyse rainfall and temperature data. As I wrote I had no idea whether I could achieve this. Well, I have managed to make some progress, and staying the in the spirit of this project, I thought I should report it here.

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I have just read an interesting piece in the Eos newspaper. This is the weekly communication newspaper from the American Geophysical Union (AGU), one of the largest academic organisations in the world. I am a member of the AGU, I will declare this up front. The link to the article is here, but I am not sure whether this is a public link. I will summarise the content.

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I must admit that this part of the series is the hardest to write and the more I read on the topic, the more I understand the problem, but more importantly, the less I can think of what the solution is.
This part of the series concentrates on structural uncertainty and what this means for the model world and how we might deal with this. In the hydrological science there is currently an active debate on the topic, with different groups of people approaching the issue from different ends.
I will continue to use the linear regression example here, but I think the discussion actually might go beyond this model. The problem is that for the linear regression model we can actually mathematically prove that the line of best fit found by minimising the sum squared of errors is indeed the optimal and mean fit and explains the most of the variation in the points. With more complex models, this is not so simple.
However, I will start with writing what I originally designed and then I will write the disclaimers.

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I finally managed to pick myself up to write another blog post. There are no apologies this time, just never got to it. So I want to pick up where I left off last time, discussing hydrological uncertainty.
The last thing I discussed was how the first uncertainty is in the actual fit of the model, or in other words, the model does not accurately predict all of the observed data, it predicts in fact the mean linear trend in the data.
Krzysztofowizc (1999) describes three main sources of uncertainty: uncertainty in the input data, uncertainty in the model structure and uncertainty in the output data. In this blogpost, I will concentrate just on the input and output uncertainty, my next blog post (and hopefully soon after this one) will deal with model uncertainty.

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The Authors

  • Willem (Hydrology Research Laboratory)

About the Blog

Aimed at generating discussion on water research and water management in Australia

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