The Long Tail of Hydroinformatics: Implementing Environmental Flows Information

in Hydrologic Information Systems

Eric Hersh, PhD, PE - Environmental Science Institute David Maidment, PhD, PE - Center for Research in Water Resources The University of Texas at Austin

AWRA E-Flows, June 25, 2013

• Supported by the NSF, 2004-2017, $12 million total for HIS

• CUAHSI has successfully designed and is deploying a national prototype Hydrologic Information System

• But CUAHSI HIS efforts to-date have largely excluded biological observations of the water environment and have largely been constrained to terrestrial hydrologic systems

http://his.cuahsi.org/

Hydroinformatics

Informatics – the science of information,

the practice of information processing, and

the engineering of information systems

Hydroinformatics – applied to water -or-

the study of the flow of information

related to the flow of water (and the entire

water environment in general)

HIS – a “services-oriented architecture for

water information”

“This effort is relatively nascent. A thorough literature review was performed as part of this research encompassing dozens of systems, tools, projects, and efforts; not one of them existed just ten years ago.”

Information Science

Water Information Value Ladder (Vertessey 2010)

Data-Information-Knowledge-Wisdom Pyramid (Rowley 2007)

Data - the raw facts

Information - gives meaning to Data

Knowledge - analyzing and synthesizing Information

Wisdom - using Knowledge to establish and achieve goals

The Water Environment

vs.

Surveys

Sensors

Continuous

Series-based

phys/ chem

Studies

Samples

Discrete

Collections-based

biol

• Physical data describe the movement of water and its properties (hundreds of physical parameters)

• Chemical data describe the constituents moving through the water (thousands of chemical constituents)

• Biological data describe the organisms inhabiting the water environment (millions of biological species)

The 3-D Data Cube

D = f {S,T,V}

Space, S

Time, T

Variables, V

s

t

Vi

D

“Where”

“What”

“When”

A data value

(Maidment 2002)

7 CUAHSI ODM (Horsburgh et al. 2008)

KINGDOM

PHYLUM

CLASS

ORDER

SUBORDER

INFRAORDER

FAMILY

GENUS

SPECIES

SUBSPECIES

Level Classification Description

Kingdom Animalia animals

Phylum Chordata chordates (possessing a nerve cord)

Subphylum Vertebrata vertebrates (with backbone and spinal column)

Class Mammalia mammals

Order Cetacea whales and dolphins

Suborder Mysticeti baleen whales (possessing baleen plates

instead of teeth)

Family Balaenopteridae rorquals (possessing pleated throat grooves)

Genus Balaenoptera finback whales

Species B. musculus Blue whale

Taxonomic Classification

Length = 98 feet

Mass = 400,000 pounds

…and Traits

D = f {S,T,V}

taxonomy

trait Measurements and characteristics, such as length, mass, sex, or count

The 4-D Data Cube

traits have species & species have traits database efficiency & query simplicity

S 62.08 °N, 19.62 °W

T July 27, 2012

V? Balaenoptera musculus (blue whale)

V? Length = 98 feet

Semantic Mediation

(Arizona Republic, 11/19/2012)

http://www.itis.gov/

reservoir discharge

streamflow

Addressing:

- how data is formatted = syntactic mediation

(ODM, WaterML, OGC)

- how data is described = semantic mediation

(Ontology, CV)

… across multiple systems

Ontologies

http://water.sdsc.edu/hiscentral/startree.aspx

Biological Taxa

Indicator Organisms

Biological Domain

Biological Community

a formal description of concepts and relationships

BioODM v.1.2 Eric S. Hersh, UT-CRWR Organism

Group Taxonomy

Sample

Method

Document

Source

Domain

Site Habitat

(has traits, ID)

(characteristics, statistics)

(link to pdf, geography)

(specific location)

(general location)

(substrate, cover, hydraulics, characteristics) (provenance,

contact info)

Weight Units

Datum

Size

BioODM

133 additions and 17 edits to the CUAHSI Controlled

Vocabulary

CV changes Taxonomy table

ODM

“…issues have arisen with regard to the lack of sufficient site-specific scientific data and analyses describing the essential relationships between environmental flows and the actual needs of aquatic organisms in those systems.”

- SB3 Science Advisory Committee to BBASCs and BBESTs, 2/17/2010

Problem:

Bring bio data into a structured format to stand on the same footing as hydrology

Solution:

An Environmental Flows Information System for Texas

• Six information types

– Observations data (WaterML/ODM)

– Geographic (SHP, KML, WFS)

– Documents (DSpace)

– Tables (conservation status, guilds)

– Tools (CaLF, HydroExcel)

– Links (Fishes of Texas, IHA, SAC)

• Four access types – Web Page

– Interactive Map Viewer

– Digital Library

– HydroPortal

The Calculator for Low Flows (CaLF)

A Services-Oriented Architecture… …for the communication of data

…via a standard language …over the internet

Not just data stored locally… …but accessible via web services for specific analysis

Here: 7Q2 and Lyons Flow Using USGS streamflow data published in WaterML

Water Information System of Systems

HIS GIS DL

WISOS

observations data geographic data digital assets

A combination of tools – multiple means of data storage, multiple

avenues of data access, water web services, maps, documents, and

databases – all aggregated into a shared data portal.

• How does this schema apply to Texas rivers?

• Purpose: (1) the information used, the questions posed, and the analyses conducted to aid in environmental flow determinations; (2) demonstrate how an improved HIS can help.

• Both taxonomy and traits are included – fish genus and species plus count and length.

Lower Sabine River Case Study

Blacktail shiner (Cyprinella venusta) Bullhead minnow (Pimephales vigilax)

Bay anchovy (Anchoa mitchilli) Spotted bass (Micropterus punctulatus)

Sabine shiner (Notropis sabinae)

Biological Physical Sampling Effort

scientific name water depth seine length

common name velocity at 20% depth haul length

count velocity at 60% depth shock distance

minimum total length velocity at 80% depth shock time

maximum total length substrate type

habitat type

cover type

embededness

5,831 fish observed representing 58 species.

On average, 729±433 fish were collected per study reach.

The average reach had 24±8 species represented.

The five most abundant species accounted for ~70% of all fishes.

Toledo Bend Dam is having a negative impact on fish abundance and species richness… …and the inland silverside has had some, but limited, success in invasion.

Environmental flows considerations: (1) how the hydropower dam is operated; (2) freshwater inflows to Sabine Lake

35% visited EFIS more than once and 10% visited more than 10 times!

Impact To-Date

Data Portal # of Visits # Unique Visitors

Time (Years)

EFIS 1603 1603 < 3

COMIDA CAB 2868 1508 < 2.5

Texas seagrass 1285 675 < 2

TOTAL 5,756 3,786

http://texasseagrass.org/ http://comidacab.org/ http://efis.crwr.utexas.edu/

• Improved understanding of how to deal with collections of biological data stored alongside sensor-based physical data

• New 4-D data cube for biological observations data with axes of space, time, species, and trait

• Reworking and expansion of the biological domain ontology

Contributions to CUAHSI HIS

• Water Information System of Systems vision for next-generation Hydrologic Information Systems

1. Geographic data stored in a geodatabase in a GIS,

2. Observations data stored in a relational database in an HIS

3. Documents stored in a digital repository in a Digital Library

Contributions to HIS

Acknowledgements

thanks.