New Mexico Supercomputing Challenge

Overflow: Louisiana and Florida Underwater

Team: 59

School: Las Cruces High

Area of Science: Climatology


Interim:

Problem Definition:

With the current global temperature and sea level rise, we must be wary of the drastic outcomes that may soon affect us. It is not just the “at risk” cities that should worry about a slowly incoming flood, but surrounding cities as well. This is because there will be mass population movements away from flood zones, which will have huge effects on economy, pollution and other factors of a city. Our planet will undoubtedly become more “water based” and we cannot change that. We can, however, predict where water will rise the most and plan accordingly.

Problem Solution:

Our simulation would be conducted: based off the branch of Statistics known as probability. To be more specific: estimation theory and stochastic differential equations. Due to uncertainty in actual sea level rise, and the effects of factors like storm surges, we have no way of measuring the exact amount water will rise. We can however give a good estimate based off of past trends and the return periods of large storms, which increase flooding. The mathematical uncertainty of these factors allows us to use Monte Carlo simulation to come up with a model that will accurately reflect possibilities in sea level rise.

Progress to Date:

Currently we have played around with MatLab and GNU Octave to try and graph what the future water levels will look like. We have decided to model Louisiana and Florida because they seem to have the most active water level rise and because they both have a very dense coastal city population. To top it all off, we have had more than 5 meetings via Skype with mentors who have helped guide us in our project.

Expected Results:

After programming, testing, and refining of the mathematical simulation our system should give a good estimate on water level rise for the future. Our model could also be further developed by showing the effect it might have in different geographic and socioeconomic areas. This will broaden the relevance of our model and ensure a broad range of uses.

Team Members:

Kevin Lee, Dante Laroche, and Andrew Lawendy.

Sponsoring Teachers: Elisa Cundiff and Lauren Curry

Citations:

http://en.wikipedia.org/wiki/Kalman_filter http://en.wikipedia.org/wiki/Probability_density_function http://www.maths.manchester.ac.uk/~scotter/MATH20401/week10.pdf http://www.ipcc.ch/publications_and_data/ar4/wg1/en/figure-spm-5.html http://www.math.dartmouth.edu/opencalc2/cole/lecture8.pdf http://en.wikipedia.org/wiki/Estimation_Theory http://en.wikipedia.org/wiki/GNU_Octave http://www.maths.manchester.ac.uk/~scotter/videos.html


Team Members:

  Dante Laroche
  Andrew Lawendy
  Hoon Jeong Lee

Sponsoring Teacher: Elisa Cundiff

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