Simon Rodgers

Content Why do we need data

Hydroinformatics

Hydrologic Data

Rainfall

Topography

Water level

Streamflow

Water quality

Conclusion

Why do we need data ? Collection of reliable hydrologic data over time and

spatially to represent the water resource and catchment

conditions

Assessment of catchment hydrology, eco-hydrology,

flow hydraulics to predict streamflows, water quality,

floods and assess sustainable yields

Inform planning and policy decisions

The Data Cycle - Hydroinformatics Generation

Editing

Storage

Analysis

Presentation

Hydroinformatics - The Data Cycle Generation

Editing

Storage

Analysis

Presentation

Record daily rainfall Missing days Store it database Prepare monthly rainfall information

Rainfall data • Point data

• Daily totals (9am to 9am) • Pluviograph (sub-daily)

• Spatial data

• Gridded data (eg, SILO, AWAP) • Radar information

Sources • Bureau of Meteorology • Department of Water • Department of Agriculture • Community

DoW BoM

Location of meteorological sites

Catchment data Topographic

Structures and drainage infrastructure

Vegetation

Soils

Land use

Topography National digital eleveation

models

1 second DEM (~30 m grid)

9 second DEM (~ 250 m grid)

Topography Survey

Field survey

Traditional

RTK (Real-time kinetic GPS and differential GPS)

Photogrammetry

Lidar

Survey Technique Nominal Accuracy (+/- m)

Vertical Horizontal

Traditional Ground Survey 0.01 0.01

RTK GPS 0.05 0.05

Photogrammetry 0.1-0.3 0.2-0.5

ALS (LiDAR) 0.15-0.4 0.2-0.5

Topography Checking aerial surveying data

Point vs Point

Point vs DTM surface

Known survey points

Topography Checking aerial surveying data

String vs DTM surface

Is standing water an issue ?

Structures and drainage infrastructure Levees;

Road and rail embankments;

Hydraulic structures such as dams, weirs, bridges and culverts; and

Drainage infrastructure such as pipes and pits.

Vegetation Aerial photography

Field survey

GIS databases

Soils and Land use Soils mapping

National scale mapping (Atlas of Australian Soils - http://www.asris.csiro.au/mapping/viewer.htm )

State based (DAFWA)

Field survey

Land use mapping

Town planning schemes (LG, DoP)

Aerial photography

Water level Periodic

Staff gauges

Bore dip

Sources • Department of Water • DAFWA • DPaW • Main Roads WA • Irrigation cooperatives

Water level Periodic

Staff gauge

Floodmarks

Water level Periodic

Staff gauge

Floodmarks

Photography

Water level Periodic

Staff gauge

Floodmarks

Photography

Satellite

Sources Landgate (Floodmap) Geoscience Australia (Water observations from space) http://landsatlook.usgs.gov/viewer.html

Water level Periodic

Staff gauge

Floodmarks

Photography /Satellite

Newspapers

Water level Continuous

Stilling well

Gas-purge

Pressure transducer

Radar

Department of Water Water Corporation DAFWA

Water level uncertainty Equipment

Resolution, repeatability, calibration

Water level uncertainty Channel stability

Robustness

Streamflow Combines information on channel

geometry and velocity

Discharge measurements

“orange peel method”

Traditional propeller

Acoustic Doppler

Stage –Discharge rating curve

Streamflow uncertainty Channel geomorphology

Stream gauge

Streamflow uncertainty Backwater

Looped ratings

Streamflow uncertainty Seasonal/inter annual changes (vegetation, fire etc)

Debris build up

Damage to control feature

Water Quality Periodic

Stage height sampling

Automated pump sampler

River health snapshots

Stationarity No gradual or abrupt changes in

dataset

Causes of change Changes in use (ie, dams,

pumping) Changes in catchment

characteristics (ie, clearing, groundwater levels)

Changes in climatic factors (ie, rainfall)

Changes in hydraulic control features (eg, bridges, levees)

Conclusion Data providers endeavour to supply highest quality

possible

Accuracy of most data is limited by the equipment used in the collection

Some data may change over time making metadata such as date of extraction important

Extremes (such as cease to flow and floods) in the data are the periods with greatest uncertainty

Conclusion All data is uncertain – Knowledge uncertain

But with extra effort we can often reduce the uncertainty