GEO4310 Statiscal and Stochastic Methods in Hydrology

Fall 2017 Course Notes

Description

GEO4310/GEO9310 is a core course for hydrology students at Master level and candidate course for PhD students. The course reviews statistical analysis methods and provides a foundation for time series analysis and stochastic modeling of geophysical time series. Theoretical material includes: Moments analysis of times series, continuous and discrete probability distributions and their applications in hydrology, frequency analysis, hypotheses testing, goodness-of-fit testing, simple and multiple regression analysis, ANOVA, time series analysis and stochastic modeling, error theory, and uncertainty analysis. We utilize tools from the data science domain to learn these concepts. You will be introduced to basic scripting in Python for data analysis

Goal: Students should have a fundamental grasp for understanding complex data related to hydrology.

After the course you will be able:

  • To perform quality control of data
  • To analyze and classify types of hydrological time series
  • To perform frequency analysis of extreme values of precipitation, floods, low flow
  • To perform hypotheses testing, goodness-of-fit testing
  • To model different types of hydrological time series (purely random, stationary, non-stationary, etc.)
  • To perform uncertainty analysis

Course Details

Overview
     
Instructor : John F. Burkhart, 326 Geosciences, (john.burkhart@geo.uio.no)
Class Time : Tue 14-16 & Thr 10-12; Fri 09-12 (lab)
Assistants : Bikas Chandra Bhattarai, (b.c.bhattarai@geo.uio.no)
Text : A compendium is provide. References to other books provided as required.
Website : Course notes are available at http://folk.uio.no/johnbur/GEO4310/index.html
Grading : Exercises/participation 50%; Written Final Examination 50%
  • Forum / Fronter

    – We will be using Fronter. See the link to the appropriate semester below for information on how to acess the site.

  • Suggested Reading

    – The course uses a compendium available through Fronter. The compendium was originally written by Professor Chong-Yu Xu and is updated yearly. Students are expected to complete all reading assignments prior to lectures. Additional reading assignments may be required – sources will be provided.

  • Software

    – Mostly we will be using Python as a scripting language for exercises. Some exercises may also be written using R. In the first lab exercise we will go through a ‘scientific computing workflow’. If you have not already acquired a ‘reproduceable science’ workflow, then I strongly encourage you to focus on it for the benefit of your academic endeavors.

  • Exercises

    – The lab exercises are a required component of the course and account for 50% of your grade. In some cases there will not be time within the course time to complete the tasks. In this case, you’ll have to continue working on these on your own. All resources for completing any exercises are available for free (e.g. the software is open source) and all required datasets are available through Fronter if they are not openly available datasets. The exercises are generally due 1 week following the lab. See the grading policy for more information.

  • Grading Policy

    – Your course grade will be based on class participation, the exercises (50%), and the final exam (50%). The exercises are due 1 week following each lab. This is a hard deadline, with the following grade reduction:

    • 1-2 days late, 5%
    • 2-4 days late, 15%
    • 3-7 days late, 30%
    • >7 days late, not accepted.

    Should you require further time for an exercise inform us in advance! Just be warned, it’s unlikely we’ll accept your excuse.

    —The exam date is posted on the UiO course homepage, and by following the appropriate semester link below. Regarding participation, there is a lot of material covered in this course – it’s wise to listen for highlighted concepts in the lectures.

  • Collaboration:

    – Working together on the exercises is strongly encouraged and permitted. However, what you submit should be your own work, and reflect your own understanding of the material. For your own benefit, do not just copy others answers or computer code. Remember, 50% of the grade is based on an exam. – Cheating is unacceptable and the final exam will ultimately reflect your own retention of information.

  • Getting help

    – Feel free to contact any of your instructors as a first resource if you are seeking extra information on an assignment or lecture.

    —You can email me, but it is likely I’ll (anonymously) post the query to the discussion forum for the benefit of others.

    – Feel free to stop by during office hours, or otherwise (rm. 326, Geosciences)