Making use of existing data in Making use of existing data in river health assessmentriver health assessment
Dr Chris Gippel, AustraliaDr Chris Gippel, Australia
Project Completion WorkshopProject Completion Workshop
Ministry of Water ResourcesMinistry of Water Resources
2012 February 232012 February 23
River health assessment
Key drivers•Hydrology
•Water quality
•Physical form
•Riparian vegetation
Indicators help informmanagers how to act
Indicators reflect management performance
•Social– Production of goods– Recreation– Drinking water– etc.
Key responses•Biological
– Fish– Macroinvertebrates– Algae
All indicator groups are useful
Data gathering programs already existData gathering programs already exist
Benefits / limitations of using existing data
Benefits
Low cost
System will continue to provide data
Quality control and staff training systems established
Data collected frequently
Historical data available
Limitations
Site location is pre-determined
Few sites in each catchment
Most sites are on main stems and large tributaries
Data have to be obtained from the program owner
For water quality, data might not contain the desired parameters
How to incorporate existing data
Hydrology and water quality station
River health monitoring site
Using the historical recordsW
ater
qu
ality
Years
Bio
logy
Years
Hyd
rolo
gy
Years
Begin riverhealth program
Using the historical recordsW
ater
qu
ality
Years
Bio
logy
Years
Hyd
rolo
gy
Years
Began e-flow Begin riverhealth program
Converting hydrology data to indicator scores
No e-flows study• Assume monthly data
available
• Assume data available before regulation - “reference” hydrology
• Calculate ecologically meaningful indicators
• Score from 0 - 1
E-flows study done• Assume daily data available
• E-flows recommended components define the “reference” hydrology
• Measure the compliance of the flow with the e-flows components
• Score from 0 - 1
Flow Health – monthly data
Taizihe – LiaoyangHigh disturbance太子河 -辽阳 , 人类干扰很大
0
100
200
300
400
500
600
700
800
Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct
Mo
nth
ly f
low
(m
3
106)
50th percentile in reference period
25th percentile in reference period
Year 1999/2000
Persistently low (PLF)and persistentlyvery low (PVL)
Persistentlyhigh (PHF)
High f low season volumegreatly reduced (HFV)
Low f low season volumegreatly reduced (LFV)
Seasonality altered (SFS)
Lowest monthly f lowgreatly reduced
from reference (LMF)
Highest monthly f lowmoderately reducedfrom reference (HMF)
Flow Health – monthly data
0
200
400
600
800
1000
1200
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug
Mo
nth
ly f
low
(m
3
106)
50th percentile in reference period
25th percentile in reference period
Year 2007/2008
Persistently lowreduced (PLF)
Persistentlyhigh OK (PHF)
High f low seasonvolume OK (HFV)
Low f low season volumeslightly reduced (LFV)
Seasonality intact (SFS)
Lowest monthlyf low OK (LMF)
Highest monthlyf low OK (HMF)
Persistently verylow OK (PVL)
Guijiang – Guilin Low disturbance桂江 -桂林,干扰较小
Score relative to reference
75th
percentile25th
percentileMaximumMinimum
Fre
qu
en
cy
of
ob
ser
va
tio
ns
in
re
fere
nc
e p
eri
od
High flow season hydrological attribute value
75th
percentile25th
percentileMaximumMinimum
Fre
qu
en
cy
of
ob
ser
va
tio
ns
in
re
fere
nc
e p
eri
od
Low flow season hydrological attribute value
V large V small
Deviation from reference range of variationSmall
ModerateLarge
Very small
Reference rangeof variation
Small
Score = 1Score = 1
Score = 0 Score = 0
Converting water quality data to indicator scores
Percent of time target met• Set a target grade (I – V)
• Calculate grade for each month
• Calculate percent of months reaching target grade
• Percent of time at target =score from 0 - 1
Weighted grade method• Weight each water quality grade (I =
1.0….V = 0.0)• Calculate grade for each month• Calculate percent of months
reaching each grade• For each grade, calculate percent of
time X weighting• Add the weighted scores =
score from 0 - 1
Summary
• Existing water quality and hydrology data help to tell the history of river health
• Using existing data is inexpensive• Existing data can be combined with data from a site monitoring
program• The indicator score calculations are very easy