Acedp november 2010 beijing workshop bond


Published on

Published in: Technology
  • Be the first to comment

  • Be the first to like this

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Acedp november 2010 beijing workshop bond

  1. 1. Additional information to supportriver health assessmentDr Nick Bond
  2. 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 another1. Quality assurance2. Site selection3. Pressure indicators4. Classification5. Refinement and adaptation
  3. 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. 4. Sources of error in assessment Scientists like to make things complicated
  5. 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. 6. sediment plumeSite selection found on the EMAP website: ( Simple random sampling• Site selection strongly influences assessment results• River health assessment best served by random or found on the EMAP website: ( 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. 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. 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. 9. 2. Pressure indicators• Good indicators respond predictably to disturbance gradients Ecological indicators Disturbance (pressure)
  10. 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. 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. 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. 13. Example - urbanisation• Impacts of urbanisation best predicted by % effective imperviousness • Water sensitive urban design reduces imperviousness
  14. 14. Pressure indicators as an element of riverhealth 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. 15. Pressure indicators as an element of riverhealth 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. 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 disturbanceRequirements of a RiverClassification to support 3. Stable groupings that assist indicator selection & target settingriver health assessment 4. Derived using explicit, repeatable and transferable methods 5. Applicable to a range of stream types
  17. 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. 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. 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
  20. 20. Minimum catchment resolution
  21. 21. Supporting datasets – ecosystem types
  22. 22. 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
  23. 23. 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