UNIQUE NUMBER: 15500
INSTRUCTOR: David R. Maidment
Office: ECJ 8.612
Phone: Campus 4714620, CRWR 4710065
Email: maidment@mail.utexas.edu
OFFICE HOURS: Tuesday and Thursday 24PM, ECJ 8.612
LECTURES: Tuesday and Thursday, 12:302PM, ECJ 5.410
OBJECTIVES: This course is designed to present an advanced understanding of:
§ The statistical characterization of water resources data
§ Variation of statistical properties in space and time
§ Analysis of water observations datasets
PREREQUISITES: CE 311S or an equivalent undergraduate course in statistical methods.
TEXT: Environmental Statistics by Vic Barnett, Wiley, Chichester England, 2004. This book can be obtained from Amazon.com or from Chips books at http://www.chipsbooks.com/envstat.htm
REFERENCES Statistical Methods in Water Resources, Techniques of Water Resources Investigations Book 4, Chapter A3, September 2002, by D.R. Helsel and R.M. Hirsch, which is available free of charge at: http://pubs.usgs.gov/twri/twri4a3/html/pdf_new.html This book can be printed at Fedex Kinkos on Medical Arts near campus by asking for the folder “Maidment”. The cost is about $50.
CLASS FORMAT: Lectures supplemented with outside reading, homework, and exams.
CLASS OUTLINE: See attached.
GRADING: Homework = 20%
Midterm Exam = 20%
Oral Term Project = 10%
Written Term Project = 30%
Final Exam = 20%
95100% A
90 95% A
8790 B+
8387 B
8083 B
7780 C+
7377 C
7073 C
If the class is taken Credit/No Credit, a grade of Credit will be assigned for a score of 80 or above.
Any problems, personal or otherwise, affecting grades should be brought to the instructor's attention.
HOMEWORK POLICY: Homework assignments are due in by 5PM on the day assigned. There is a box outside my door in ECJ 8.6 for turning in assignments after the class hour, if necessary. Homework must be done on clean paper, stapled in the top left corner, have your name in the top right corner, and your name, class and assignment number written on the outside when the homework is folded in half.
EXAMINATIONS: There will be one 75 minute in class
examination and a final examination. Each examination will be closed book,
although you will be allowed a 1page review sheet, and will be given on the
date and time indicated. Missed examinations may be made up only if the reason
for missing was illness or some other emergency.
EVALUATION: The University Measurement and
DISHONESTY: University procedures will be followed in dealing with cases of suspected scholastic dishonesty.
ATTENDANCE: Regular class attendance is expected in
accordance with The University's General Information catalog and the
IMPORTANT
NOTE: The
Term Project
The purposes of the
term project are:
The steps in carrying
out the project are:
If you would like to work in a group to pursue
a term project, that is fine, but you must carry out a particular section of
the project on which you will present your oral and written report.
Key dates are shown in italics in the schedule below.
Class 
Date 
Topic 
1 
Tues Jan 20 
Class rescheduled 
2 
Thurs Jan 22 
Introduction to Statistics in Water Resources 
3 
Tues Jan 27 
How do we visualize and characterize data? 
4 
Thurs Jan 29 
How do we visualize and characterize data? 
5 
Tues Feb 3 
How are data described by probability functions? 
6 
Thurs Feb 5 
How are data described by probability functions? 
7 
Tues Feb 10 
Is a dataset
homogeneous? 
8 
Thurs Feb 12 
Is a dataset
homogeneous?

9 
Tues Feb 17 
How do we deal with correlation? 
10 
Thurs Feb 19 
How do we deal with correlation? Project proposal
posted on your web site 
11 
Tues Feb 24 
How do we deal with trends? 
12 
Thurs Feb 26 
How do we deal with trends? 
13 
Tues Mar 3 
How do we characterize diurnal and
seasonal cycles? 
14 
Thurs Mar 5 
How do we characterize diurnal and seasonal cycles? 
15 
Tues Mar 10 
Review for Midterm Exam 
16 
Thurs Mar 12 
Midterm exam 

Spring Break! 

17 
Tues Mar 24 
How are flow and water quality related? 
18 
Thurs Mar 26 
How are flow and water quality related? Project update
posted on your web site 
19 
Tues Mar 31 
How large are hydrologic extremes? 
20 
Thurs Apr 2 
How large are hydrologic extremes? 
21 
Tues Apr 7 
How do we statistically characterize
patterns in space? 
22 
Thurs Apr 9 
How do we statistically characterize patterns in space? 
23 
Tues Apr 14 
How do we characterize patterns in space and time? 
24 
Thurs
Apr 16 
How do we characterize patterns in space
and time?

25 
Tues Apr 21 
Statistical studies of Texas water resources
(Will Asquith)

26 
Thurs Apr 23 
Statistical studies of Texas water resources
(Will Asquith)

27 
Tues Apr 28 
Term Project presentations

28 
Thurs Apr 30 
Term Project presentations

29 
Tues May 5 
Term Project presentations 
30 
Thurs May 7 
Course instructor evaluation, term project presentations, and review for
the final exam

Key
Question:
How can we take large bodies of
observational data and make inferences about their properties and
interrelationships – in space, in time, and between one variable and
another?
Topic
Questions
1. How do we visualize data and determine their
statistical characteristics?
·
Time series, maps, histograms, XY
plots, Box and Whisker plots, parametric and nonparametric statistics.
2. How
do we describe a dataset by a probability distribution?
·
Determining parameters, fitting
distributions, regular and log distributions.
3. Is
a body of data statistically homogeneous?
·
Is one subset of the data different from
another?
4. How
do we deal with correlation?
·
Of one variable with another? In time?
In space?
5. How
do we deal with trends?
·
Trends due to population growth.
6. How
do we deal with cyclical variations?
·
Diurnal and seasonal cycles.
7. What
is the relationship between flow and water quality?
·
Flow, concentration and load and their
interrelationships.
8. How
do we characterize hydrologic extremes?
·
Order statistics, frequency analysis
9. How
do we statistically characterize variations in space?
·
Geostatistics,
kriging, variograms
10. How
do we characterize spatial patterns that vary in time?
·
Severe storms
The
class is divided into groups to help deal with topics 210. What I would like each group to do is to
research out computer methods that can be used by the class to deal with that
question, a dataset that can be used to illustrate that question, and write a
short exercise using the computer methods to do the data analysis. I would like to have this information in
hand by the Tuesday of the week in which this question is to be addressed in
class. Where possible, I would like to
use Excel, SAS and ArcGIS
as the computational systems in the class.