This document discusses additional considerations for river health assessment, including quality assurance, site selection, pressure indicators, classification, and refinement over time. Quality assurance requires scientific basis, quality control, data interpretation, and evaluation of results. Site selection is important and best done randomly or stratified randomly. Pressure indicators can predict health and identify high risk areas. Classification accounts for natural variation and aids in indicator selection. River assessments evolve over many years through refinement.

1. Additional information to support
river health assessment
Dr Nick Bond

2. Talk outline
• Some additional ideas to think about in relation
to river health assessment.
– Things we may have overlooked in other talks
– Not necessarily linked to one another
1. Quality assurance
2. Site selection
3. Pressure indicators
4. Classification
5. Refinement and adaptation

3. 1. Quality assurance
• Managers and the public expect a high level of
confidence in assessments.
• Requires
– A scientific basis for the assessment
– High standards of quality control & quality assurance (QA/QC)
• Field and laboratory work
• Data analysis and data storage
• Data interpretation
– Careful evaluation of the results (do they make sense?)

4. Sources of error in assessment
Scientists like to
make things
complicated

5. QA/QC – staff training
• Appropriate training and testing of
staff involved in field and
laboratory work
• EPA (Australia) ID 20% of
invertebrate samples twice for
consistency.
• Development of standard
operating procedures
• May involve collaboration between
organisations - universities,
research groups, central &
provincial govt.

6. sediment plume
Site selection found on the EMAP website:
(http://www.epa.gov/nheerl/arm/designpages/design&analysis.htm).
Simple random sampling
• Site selection strongly
influences assessment
results
• River health assessment
best served by random or
found on the EMAP website:
(http://www.epa.gov/nheerl/arm/designpages/design&analysis.htm).
stratified random site Simple random sampling Stratified random sampling
selection
strata
– Guided by classification
and assessment of threats
• Worthwhile to develop
clear guidelines before
going into the field Stratified random sampling FIGURE 3-1. Examples of two-dimensional probabilistic sampling
designs.
strata
Quantifying trends in resource condition is often an important objective for regiona
Although there are different approaches for allocating sampling effort over time, o
covered in this document: permanent station and serially alternating (Rathbun 1999
station approaches use a random sample of n sites that are all sampled during each

7. Data analysis and storage
• Relatively complex datasets
– Large # of variables
– Taxonomy may change over
time
– Biological indicators often
derived from a series of
calculations
Importance of good data
management typically
overlooked whereas good
QA/QC demands it

8. Evaluating results
• Indicators are not perfect – sometimes results
will conflict with expectations
– Requires transparent process of review and
refinement.
– Often several possibilities
• Sampling error, natural disturbance effects (e.g. floods),
localised pollution event.
– Local expertise/input is valuable
– Look to explain conflicting patterns rather than
simply discarding the result.

9. 2. Pressure indicators
• Good indicators respond predictably to
disturbance gradients
Ecological indicators
Disturbance (pressure)

10. Pressure indicators
• Good indicators respond predictably
to disturbance gradients
• Why not just measure the
disturbance gradient to predict river
health?
Ecological indicators
Disturbance (pressure)

11. Pressure indicators
• Good indicators respond predictably
to disturbance gradients
• Why not just measure the
disturbance gradient to predict river
health?
Ecological indicators
Two answers:
1. Sometimes we do
Disturbance (pressure)

12. Pressure indicators
• Good indicators respond predictably
to disturbance gradients
• Why not just measure the
disturbance gradient to predict river
health? Same
pressure
Ecological indicators
different
Two answers: management
1. Sometimes we do
2. Ecosystem health can Improved
improve if best practice management
management actions are
implemented Disturbance (pressure)

13. Example - urbanisation
• Impacts of urbanisation best
predicted by % effective
imperviousness
• Water sensitive urban design
reduces imperviousness

14. Pressure indicators as an element of river
health assessment
• Generally desktop based
analyses
• Data increasingly
available at fine scales
• May incorporate a range
of threats (e.g.)
– Population density
– Agricultural production
– Agricultural water use Forest
– Upstream/downstream Grasslan
d
impoundments Wetland
agricultur
e
urban

15. Pressure indicators as an element of river
health assessment
• Help Identify ‘high risk’
areas
• Guides site selection –
stratification by land-use
• Testing indicators
River Disturbance Index =
Upstream Storage Volume
• Effective communication weighted by Catchment Area
tool

16. 3. Classification
• River health assessment must account for
natural variation in rivers
• River classification an important step in most
assessment programs
• Identifying appropriate indicators
• Scoring (targets and thresholds)
1. Discriminates ecologically meaningful variation in indicators
2. Based on data unaffected by human disturbance
Requirements of a River
Classification to support 3. Stable groupings that assist indicator selection & target setting
river health assessment 4. Derived using explicit, repeatable and transferable methods
5. Applicable to a range of stream types

17. Other applications of classification
• Identifying ‘comparable’ catchments outside of
the study basin
– Assessing transferability of published data to/from
different regions based on similarity in classification

18. Caveats on classification results
• Important to confirm classification is biologically
meaningful
– Most software will produce ‘classes’ even if these
are extremely similar to one another.
– Possibly try a range of classification algorithms and
input variables to determine sensitivity of the results
to particular attributes (e.g. soils, temperature,
rainfall).

19. USGS Hydro 1K global dataset
• DEM stream network
• Nested subcatchment codes (Pfaffstetter,
1989)
• Allows linking of catchment data without complex
geoprocessing
• not as fine-scaled as some catchment
delineations so less well suited to local
analyses.
• Climate, ecosystem type layers also available
at global/national scales

23. Minimum catchment resolution

24. Supporting datasets – ecosystem types

25. 4. Refinement and adaptation
• River health monitoring and environmental flows
assessment strategies will evolve over time
• 15+ years in Australia (>20 years in USA)
– Changes in indicators
– Refinement of targets and scoring systems
– Continues to evolve
• Many good Chinese examples of applying these
methods in a research context
• Incorporating into management is the next step

26. e process
Conceptual
models
Land-use
Identify suite of Assess indicator assessment to
potential indicators Field trial sensitivity to define
disturbance disturbance
gradient gradient
Consider for yes Did the indicator No
respond as
Review
inclusion in expected indicator
scorecard
Do standards already No Can thresholds and
exist (chinese or targets be established
international) from the data? No
yes
Yes
Consider for
River Adopt appropriate Include in future
Classification standard scorecard programs