1. CE 394K.2 Surface Water Hydrology
Lecture 1 – Introduction to the course
Readings for today
Applied Hydrology, Chapter 1
“Once more unto the breach, dear friends, once more;
Or close the wall up with our English dead!
In peace there’s nothing so becomes a man
As modest stillness and humility:
But when the blast of war blows in our ears,
Then imitate the action of the tiger…..”
King Henry V before the battle of Agincourt, 1415
Shakespeare, King Henry the Fifth, Act III, Scene I

2. How is new knowledge discovered?
After completing this Handbook in 1993, I asked myself the question: how is new knowledge discovered in hydrology? I concluded that there are three ways:
By deduction from existing knowledge
By experiment in a laboratory
By observation of the natural environment

3. Deduction – Newton
Deduction is the classical path of mathematical physics
Given a set of axioms
Then by a logical process
Derive a new principle or equation
In hydrology, the St Venant equations for open channel flow and Richard’s equation for unsaturated flow in soils were derived in this way.
Three laws of motion and law of gravitation
(1687)

4. Foundations of scientific medicine
Experiment – Pasteur
Experiment is the classical path of laboratory science – a simplified view of the natural world replicated under controlled conditions
In hydrology, Darcy’s law for flow in a porous medium was found this way.
Pasteur showed that microorganisms cause disease & discovered vaccination
Foundations of scientific medicine

5. Observation – Darwin
Observation – direct viewing and characterization of patterns and phenomena in the natural environment
In hydrology, Horton discovered stream scaling laws by interpretation of stream maps
Published Nov 24, 1859
Most accessible book of great
scientific imagination ever written

6. Conclusion for Hydrology
Deduction and experiment are important, but hydrology is primarily an observational science
discharge, water quality, groundwater, measurement data collected to support this (USGS)

7. Hydrologic Science and Engineering
Hydrologic Processes
(inferred)
Physical environment
(characterized)
Hydrologic conditions
(observed)
Hydrologic
Science
In science, we observe conditions and infer processes
In engineering, we simulate processes and predict conditions
Both require characterizing the surrounding environment
Hydrologic Processes
(simulated)
Physical environment
(characterized)
Hydrologic conditions
(predicted)
Hydrologic
Engineering

8. Hydrologic Science
It is as important to represent hydrologic environments precisely with
data as it is to represent hydrologic processes with equations
Physical laws and principles
(Mass, momentum, energy, chemistry)
Hydrologic Process Science
(Equations, simulation models, prediction)
Hydrologic conditions
(Fluxes, flows, concentrations)
Hydrologic Information Science
(Observations, data models, visualization
Hydrologic environment
(Physical earth)

9. Water quantity and quality
Water Data
Water quantity and quality
Soil water
Rainfall & Snow
Modeling
Meteorology
Remote sensing

10. Water Data Web Sites

11. NWISWeb site output Time series of streamflow at a gaging station
# agency_cd Agency Code
# site_no USGS station number
# dv_dt date of daily mean streamflow
# dv_va daily mean streamflow value, in cubic-feet per-second
# dv_cd daily mean streamflow value qualification code #
# Sites in this file include:
# USGS NEUSE RIVER NEAR CLAYTON, NC
agency_cd site_no dv_dt dv_va dv_cd
USGS
USGS
USGS
USGS
USGS
USGS
USGS
USGS
USGS
USGS
USGS
Time series of
streamflow at a
gaging station
USGS has committed
to supporting CUAHSI’s
GetValues function

12. Observation Stations Map for the US Ameriflux Towers (NASA & DOE)
NOAA Automated Surface Observing System
David - for each of these site files, can you include how many points are in each one.
USGS National Water Information System
NOAA Climate Reference Network

13. Water Quality Measurement Sites in EPA Storet
Substantial variation in data availability from states
Data from Bora Beran, Drexel University

14. Water Quality Measurement Sites from
Texas Commission for Environmental Quality (TCEQ)

15. Geographic Integration of Storet and TCEQ Data in HIS

16. and how many observations of each variable are available
Observations Catalog
Specifies what variables are measured at each site, over what time interval,
and how many observations of each variable are available

17. CUAHSI Hydrologic Data Access System
(being built using HIS Server in collaboration with ESRI)
NCDC
NASA
EPA
NWS
Observatory Data
USGS
David – This slide implies that the CUAHSI website is the path through which people would access this data. I’m not sure that this is really your intent, since the fed agencies are not CUAHSI members and in the long run would probably access this data and use these tools through another path.
But the line at the bottom of the slide should appear somewhere, as it is one of the key messages, if not THE key message.
How do you feel about changing the labeling on this slide to be more consistent since it’s a fed agency audience. Either list agencies, list data sources, or list both.
So USGS could be listed as USGS, as NWIS, or as USGS NWIS
EPA as EPA, STORET or EPA STORET
NCDC as NOAA, NCDC or NOAA NCDC
NWS as NOAA-NWS or NDFD, or NOAA NDFD
NASA – any specific datasets or websites like Giovanni?
A common data window for accessing, viewing and downloading hydrologic information

18. HIS Server Supports data discovery, delivery and publication
Data discovery – how do I find the data I want?
Map interface and observations catalogs
Metadata based Search
Data delivery – how do I acquire the data I want?
Use web services or retrieve from local database
Data Publication – how do I publish my observation data?
Use Observations Data Model

19. HIS Server and Analyst HIS Server HIS Analyst
Implemented at San Diego Supercomputer Center and at academic departments and research centers
Implemented by individual hydrologic scientists using their own analysis environments
Web
Services
Flexible – any operating system, model,
programming language or application
Sustainable – industrial
strength technology
Details of HIS Analyst are here

20. Point Observations Information Model
USGS
Data Source
Streamflow gages
Network
Neuse River near Clayton, NC
Sites
Discharge, stage
(Daily or instantaneous)
Variables
Values
206 cfs, 13 August 2006
{Value, Time, Qualifier}
A data source operates an observation network
A network is a set of observation sites
A site is a point location where one or more variables are measured
A variable is a property describing the flow or quality of water
A value is an observation of a variable at a particular time
A qualifier is a symbol that provides additional information about the value

21. Data Sources Extract Transform CUAHSI Web Services Load Applications
NASA
Storet
Ameriflux
Extract
NCDC
Unidata
NWIS
NCAR
Transform
CUAHSI Web Services
Excel
Visual Basic
Shauna – the ones on top are a bunch of web sites providing data that go through the CUAHSI Web Services (basically some software) which allows all this data to be used in all these other software packages listed across the bottom. The ones in red are the ones that are connections that are already built. It would look good to redo this in ESRI style.
ArcGIS
C/C++
Load
Matlab
Fortran
Access
Java
Applications
Some operational services

22. CUAHSI Web Services Your application Your operating system
Web Application: Data Portal
Your application
Excel, ArcGIS, Matlab
Fortran, C/C++, Visual Basic
Hydrologic model
…………….
Your operating system
Windows, Unix, Linux, Mac
Internet
Simple Object Access Protocol
Web Services
Library

23. Series and Fields Features Series – ordered sequence of numbers
Point, line, area, volume
Discrete space representation
Time series – indexed by time
Frequency series – indexed by frequency
Surfaces
Fields – multidimensional arrays
Doug: Could you please add a point, line, area symbols or something generic that represents features.
Continuous space representation
Scalar fields – single value at each location
Vector fields – magnitude and direction
Random fields – probability distribution

24. North American Regional Reanalysis of Climate
Precipitation
Evaporation
Variation during the day, July 2003
NetCDF format
mm / 3 hours

25. Data Cube – What, Where, When
Space, L
Time, T
Variable, V D
“When”
A data value
“Where”
“What”

26. Continuous Space-Time Data Model -- NetCDF
Time, T
Coordinate
dimensions
{X} D
Space, L
Variable dimensions
{Y}
Variables, V

27. Discrete Space-Time Data Model
Time, TSDateTime
TSValue
Space, FeatureID
Variables, TSTypeID

28. Hydrologic Statistics
Time Series Analysis
Geostatistics
Multivariate analysis
How do we understand space-time correlation fields
of many variables?

29. Project sponsored by the European Commission to promote integration of water models within the Water Framework Directive
Software standards for model linking
Uses model core as an “engine”

30. OpenMI – Links Data and Simulation Models
Simple River Model
Trigger (identifies what value should be calculated)
CUAHSI Observations Data Model
as an OpenMI component

31. Typical model architecture
Model application
Application
User interface + engine
Engine
Simulates a process – flow in a channel
Accepts input
Provides output
Model
An engine set up to represent a particular location e.g. a reach of the Thames
User interface
Write
Input data
Run
Read
An engine simulates a process – water flowing in a channel
An engine +data is a model of a particular process – the Rhine
Engine
Write
Output data

32. Linking modelled quantities
Rainfall Runoff Model
River Model
Accepts
Provides
Upstream Inflow
(m3/s)
Outflow
Lateral inflow
Abstractions
Discharges
Accepts
Provides
Rainfall
(mm)
Runoff
(m3/s)
Temperature
(Deg C)
Evaporation

33. Data transfer at run time
Rainfall runoff
Output data
Input data
User interface
River
GetValues(..)

34. Models for the processes
Rainfall (database) RR
(Sobek-Rainfall
-Runoff)
River
(InfoWorks RS)
Sewer
(Mouse)

35. Data exchange 3 Rainfall.GetValues 4 2 RR.GetValues
Rainfall (database) 4 RR
(Sobek-Rainfall
-Runoff)
2 RR.GetValues
1 Trigger.GetValues 5
7 RR.GetValues 8
River
(InfoWorks-RS)
call
Sewer
(Mouse)
6 Sewer.GetValues 9
data

36. Water OneFlow
Like Geospatial OneStop, we need a “Water OneFlow” – a common window for water data and models
Advancement of water science is critically dependent on integration of water information
Federal
Academic
Local
State