CE 394 K 2 Surface Water Hydrology

CE 394 K. 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

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

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 http: //en. wikipedia. org/wiki/Isaac_Newton (1687)

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 http: //en. wikipedia. org/wiki/Louis_Pasteur

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

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)

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

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)

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

Water Data Web Sites

NWISWeb site output # 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 02087500 NEUSE RIVER NEAR CLAYTON, NC # agency_cd site_no dv_dt dv_va dv_cd USGS 02087500 2003 -09 -01 1190 Time series of USGS 02087500 2003 -09 -02 649 USGS 02087500 2003 -09 -03 525 streamflow at a USGS 02087500 2003 -09 -04 486 gaging station USGS 02087500 2003 -09 -05 733 USGS 02087500 2003 -09 -06 585 USGS has committed USGS 02087500 2003 -09 -07 485 to supporting CUAHSI’s USGS 02087500 2003 -09 -08 463 Get. Values function USGS 02087500 2003 -09 -09 673 USGS 02087500 2003 -09 -10 517 USGS 02087500 2003 -09 -11 454

Observation Stations Map for the US Ameriflux Towers (NASA & DOE) NOAA Automated Surface Observing System USGS National Water Information System NOAA Climate Reference Network

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

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

Geographic Integration of Storet and TCEQ Data in HIS

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

CUAHSI Hydrologic Data Access System (being built using HIS Server in collaboration with ESRI) EPA USGS NCDC NASA NWS Observatory Data A common data window for accessing, viewing and downloading hydrologic information

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

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

Point Observations Information Model http: //www. cuahsi. org/his/webservices. html 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

Data Sources Storet Extract NASA Ameriflux NCDC Unidata NWIS NCAR Transform CUAHSI Web Services Excel Visual Basic C/C++ Arc. GIS Load Matlab Applications http: //www. cuahsi. org/his/ Fortran Access Java Some operational services

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

Series and Fields Features Series – ordered sequence of numbers Point, line, area, volume Discrete space representation Surfaces Time series – indexed by time Frequency series – indexed by frequency Fields – multidimensional arrays Continuous space representation Scalar fields – single value at each location Vector fields – magnitude and direction Random fields – probability distribution

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

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

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

Discrete Space-Time Data Model Time, TSDate. Time TSValue Space, Feature. ID Variables, TSType. ID

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

• 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” • http: //www. open. MI. org

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

Typical model architecture Model application User interface Write Input data Read Engine Write Output data Run 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

Linking modelled quantities Rainfall Runoff Model Accepts Provides Rainfall (mm) Runoff (m 3/s) Temperature (Deg C) Evaporation (mm) River Model Accepts Provides Upstream Inflow (m 3/s) Outflow (m 3/s) Lateral inflow (m 3/s) Abstractions (m 3/s) Discharges (m 3/s)

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

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

Data exchange 3 Rainfall. Get. Values Rainfall (database) 4 RR (Sobek-Rainfall -Runoff) 2 RR. Get. Values 1 Trigger. Get. Values 5 8 7 RR. Get. Values River (Info. Works-RS) call 9 Sewer (Mouse) 6 Sewer. Get. Values data

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